From c50eebc1089335375e052e32c291ad4ba47d54d6 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 15:34:13 +0000 Subject: [PATCH 1/9] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20cv?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow2/contrib/cv/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow2/contrib/cv/.keep diff --git a/TensorFlow2/contrib/cv/.keep b/TensorFlow2/contrib/cv/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From db830c64c378b272b7469a65333c8c6390ddc786 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 15:39:49 +0000 Subject: [PATCH 2/9] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20KEYPOSE=5Ffor=5FTensor?= =?UTF-8?q?flow2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TensorFlow2/contrib/cv/KEYPOSE_for_Tensorflow2/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_for_Tensorflow2/.keep diff --git a/TensorFlow2/contrib/cv/KEYPOSE_for_Tensorflow2/.keep b/TensorFlow2/contrib/cv/KEYPOSE_for_Tensorflow2/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From ba24797e5fa3bd7a01fb3fa51153049a666152a4 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 15:55:11 +0000 Subject: [PATCH 3/9] =?UTF-8?q?=E9=87=8D=E5=91=BD=E5=90=8D=20TensorFlow2/c?= =?UTF-8?q?ontrib/cv/KEYPOSE=5Ffor=5FTensorflow2=20=E4=B8=BA=20TensorFlow2?= =?UTF-8?q?/contrib/cv/KEYPOSE=5FID2043=5Ffor=5FTensorflow2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename TensorFlow2/contrib/cv/{KEYPOSE_for_Tensorflow2 => KEYPOSE_ID2043_for_Tensorflow2}/.keep (100%) diff --git a/TensorFlow2/contrib/cv/KEYPOSE_for_Tensorflow2/.keep b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/.keep similarity index 100% rename from TensorFlow2/contrib/cv/KEYPOSE_for_Tensorflow2/.keep rename to TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/.keep -- Gitee From dd37e75cf83d372a4a14014efecb9fff814f7e49 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 15:59:48 +0000 Subject: [PATCH 4/9] keypose_tf2 Signed-off-by: Victory_ztk --- .../KEYPOSE_ID2043_for_Tensorflow2/README.md | 156 +++ .../__init.py__ | 0 .../KEYPOSE_ID2043_for_Tensorflow2/data.proto | 83 ++ .../data_pb2.py | 816 ++++++++++++ .../download_models.sh | 18 + .../estimator.py | 192 +++ .../gen_tfrecords.py | 317 +++++ .../KEYPOSE_ID2043_for_Tensorflow2/inputs.py | 458 +++++++ .../KEYPOSE_ID2043_for_Tensorflow2/losses.py | 367 +++++ .../cv/KEYPOSE_ID2043_for_Tensorflow2/nets.py | 415 ++++++ .../KEYPOSE_ID2043_for_Tensorflow2/predict.py | 214 +++ .../requirements.txt | 6 + .../cv/KEYPOSE_ID2043_for_Tensorflow2/run.sh | 25 + .../run_test_load.py | 43 + .../show_keypoints.py | 113 ++ .../KEYPOSE_ID2043_for_Tensorflow2/trainer.py | 201 +++ .../KEYPOSE_ID2043_for_Tensorflow2/utils.py | 1175 +++++++++++++++++ 17 files changed, 4599 insertions(+) create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/README.md create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/__init.py__ create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data.proto create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data_pb2.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/download_models.sh create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/estimator.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/gen_tfrecords.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/inputs.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/losses.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/nets.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/predict.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/requirements.txt create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run.sh create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run_test_load.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/show_keypoints.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/trainer.py create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/utils.py diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/README.md b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/README.md new file mode 100644 index 000000000..37ce81411 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/README.md @@ -0,0 +1,156 @@ +## KeyPose: Pose Estimation with Keypoints + +This repository contains Tensorflow 2 models and a small set of +labeled transparent object data from the +[KeyPose project](https://sites.google.com/corp/view/keypose/). There +are sample programs for displaying the data, running the models to +predict keypoints on the data, and training from data. + +The full dataset can be downloaded using the directions at the end of this README. It contains stereo and depth image sequences (720p) of 15 small transparent objects in 5 categories (ball, bottle, cup, mug, heart, tree), against 10 different textured backgrounds, with 4 poses for each object. There are a total of 600 sequences with approximately 48k stereo and depth images. The depth images are taken with both transparent and opaque objects in the exact same position. All RGB and depth images are registered for pixel correspondence, the camera parameters and pose are given, and keypoints are labeled in each image and in 3D. + +## Setup and sample programs + +To install required python libraries (running in directory +above `keypose/`): +``` +pip3 install -r keypose/requirements.txt +``` + + +To look at images and ground-truth keypoints (running in directory +above `keypose/`): +``` +$ python3 -m keypose.show_keypoints keypose/data/bottle_0/texture_5_pose_0/ keypose/objects/bottle_0.obj +``` + +To predict keypoints from a model (running in directory above +`keypose/`), first download the models: +``` +keypose/download_models.sh +``` +Then run the `predict` command: +``` +$ python3 -m keypose.predict keypose/models/bottle_0_t5/ keypose/data/bottle_0/texture_5_pose_0/ keypose/objects/bottle_0.obj +``` + +## Repository layout + +- top level: contains the sample programs. +- `configs/` contains training configuration files. +- `data/` contains the transparent object data. +- `models/` contains the trained Tensorflow models. +- `objects/` contains simplified vertex CAD files for each object, for use in display. +- `tfrecords/` contains tfrecord structures for training, created from + transparent object data + +### Data directory structure and files. + +The directory structure for the data divides into one directory for each object, with sub-directories +for each texture/pose sequence. Each sequence has about 80 images, numbered sequentially with a prefix. +``` + bottle_0/ + texture_0_pose_0/ + 000000_L.png - Left image (reference image) + 000000_L.pbtxt - Left image parameters + 000000_R.png - Right image + 000000_R.pbtxt - Right image parameters + 000000_border.png - Border of the object in the left image (grayscale) + 000000_mask.png - Mask of the object in the left image (grayscale) + 000000_Dt.exr - Depth image for the transparent object + 000000_Do.exr - Depth image for the opaque object + ... +``` + +### Model naming conventions. + +In the `models/` directory, there are a set of sub-directories containing TF KeyPose models trained for individual and category predictions. +``` + bottle_0_t5/ - Trained on bottle_0, leaving out texture_5_pose_* data + bottle_1_t5/ + ... + bottles_t5/ - Trained on all bottles, leaving out texture_5_pose_* data + bottles_cups_t5/ + mugs_t5/ + + mugs_m0/ - Trained on all mugs except mug_0 +``` + +So, for example, you can use the `predict.py` program to run the `mugs_m0` model against any of the sequences in `data/mug_0/`, +to show how the model performs against a mug it has not seen before. Similarly, running the model `bottle_0_t5` against +any sequence in `data/bottle_0/texture_5_pose_*` will do predictions against the test set. + +## Downloading data and models. + +Data and models are available publicly on Google Cloud Storage. + +To download all the models, use: +``` +keypose/download_models.sh +``` +This will populate the `models/` directory. + +The image files are large, and divided up by object. To get any individual object, use: +``` +wget https://storage.googleapis.com/keypose-transparent-object-dataset/.zip +``` +Then unzip at the `keypose/` directory, and it will populate the appropriate `data/` directories. +These are the 15 available objects: +``` +ball_0 +bottle_0 +bottle_1 +bottle_2 +cup_0 +cup_1 +mug_0 +mug_1 +mug_2 +mug_3 +mug_4 +mug_5 +mug_6 +heart_0 +tree_0 +``` + +## Training a model. + +To train a model, the data must be put into tfrecord format, and then grouped +according to train and test records. There are programs below that will do +this. For a quick start, you can download a set of tfrecords for training +a bottle_0 model that uses texture 5 as the test set. + +To get the sample tfrecords (3.4 GB), use the following: +``` +wget https://storage.googleapis.com/keypose-transparent-object-dataset/tfrecords.zip +``` +Then unzip at the `keypose/` directory, and it will populate the appropriate +`tfrecords/` directories. + +To train, use this command (running in directory above `keypose/`): +``` +$ python3 -m keypose.trainer configs/bottle_0_t5 /tmp/model +``` + +This will run the training using Estimators, and put checkpoints and saved +models into `/tmp/model`. It helps to have a good GPU with lots of memory. +If you run out of memory on the GPU, reduce the batch size in +`configs/default.yaml` from 32 to 16 or 8. Periodically the trainer will +write out models in the saved_model format. + +## Generating tfrecords from image directories + +To put images and associated metadata into tfrecord format, suitable +for training, use the following command (running in directory above `keypose/`): +``` +$ python3 -m keypose.gen_tfrecords configs/tfset_bottle_0_t5 +`` + +The configuration file `configs/tfset_bottle_0_t5.pbtxt` is a protobuf +that names the tfrecord output directory, as well as the directory +paths for inputs for train and val. It also lets you resize the +original images -- for example, the sample tfrecords used in training +are resized to 756x424, with a camera focal length of 400 pixels. + +The output of the process is in `keypose/tfrecords//tfrecords`. +This directory is suitable for input to `trainer.py`. diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/__init.py__ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/__init.py__ new file mode 100644 index 000000000..e69de29bb diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data.proto b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data.proto new file mode 100644 index 000000000..3ea2a898c --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data.proto @@ -0,0 +1,83 @@ +// Copyright 2022 The Google Research Authors. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +syntax = "proto2"; + +package keypose; + +// 4x4 transform matrix, row major order. +// Transform is cam_T_world, from world to camera coordinates. +message Transform { + repeated float element = 1; +} + +message Camera { + optional float fx = 1; // In pixels. + optional float fy = 2; + optional float cx = 3; + optional float cy = 4; + optional float baseline = 5; // In meters. + optional float resx = 6; // Image resolution in pixels. + optional float resy = 7; +} + +// Targets are assocated with an image: +// Transform from world to camera, cam_T_world. +// Camera parameters. +// Keypoints in the image. +message Target { + optional keypose.Transform transform = 1; + optional keypose.Camera camera = 2; + + message KeyPoint { + optional float u = 1; // Image coords (pixels) of kp. + optional float v = 2; + optional float x = 3; // x,y are normalized image coords of kp, NOT 3D x,y. + optional float y = 4; // Normalized coords are in [0, 1]. + optional float z = 5; // Distance from camera (m) of kp. + optional float visible = 6 [default = 1.0]; // 1.0 if kp is onscreen. + } + + repeated KeyPoint keypoints = 3; +} + +// Keypoint and projection targets for an image. +// This is the top-level pb for stereo samples. +message KeyTargets { + optional keypose.Target kp_target = 1; + repeated keypose.Target proj_targets = 2; + optional bool mirrored = 3 [default = false]; +} + +// Dataset parameters. +message DataParams { + // TODO: resx, resy are redundant given the camera. + optional int32 resx = 1; + optional int32 resy = 2; + optional int32 num_kp = 3; + optional keypose.Camera camera = 4; +} + +// TFRecord sets for training and evaluation +message TfSet { + optional string name = 1; // Output will be in keypose/tfrecords/. + optional string common_dir = 2; // Directory prefix common to all files, + // e.g. keypose/data/bottle_0. + repeated string train = 3; // Training tfrecord dirs (e.g. */756x424_fl400). + repeated string val = 4; // Val tfrecord dirs (texture_5_*/756x424_fl400). + // Note: val dirs are excluded from train dirs. + repeated string exclude = 5; // Exclude these dirs from train/val. + optional string image_size = 6; // Resized image, e.g., 756x424_fl400. + // fl400 means focal length 400 pixels. +} diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data_pb2.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data_pb2.py new file mode 100644 index 000000000..52784c381 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data_pb2.py @@ -0,0 +1,816 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# source: data.proto +# pylint: disable=bad-continuation +# pylint: disable=protected-access + +"""Generated protocol buffer code.""" + +from google.protobuf import descriptor as _descriptor +from google.protobuf import message as _message +from google.protobuf import reflection as _reflection +from google.protobuf import symbol_database as _symbol_database +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + +DESCRIPTOR = _descriptor.FileDescriptor( + name='data.proto', + package='keypose', + syntax='proto2', + serialized_options=None, + create_key=_descriptor._internal_create_key, + serialized_pb=b'\n\ndata.proto\x12\x07keypose\"\x1c\n\tTransform\x12\x0f\n\x07\x65lement\x18\x01 \x03(\x02\"f\n\x06\x43\x61mera\x12\n\n\x02\x66x\x18\x01 \x01(\x02\x12\n\n\x02\x66y\x18\x02 \x01(\x02\x12\n\n\x02\x63x\x18\x03 \x01(\x02\x12\n\n\x02\x63y\x18\x04 \x01(\x02\x12\x10\n\x08\x62\x61seline\x18\x05 \x01(\x02\x12\x0c\n\x04resx\x18\x06 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full_name='keypose.Camera', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='fx', + full_name='keypose.Camera.fx', + index=0, + number=1, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='fy', + full_name='keypose.Camera.fy', + index=1, + number=2, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='cx', + 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is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='resx', + full_name='keypose.Camera.resx', + index=5, + number=6, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='resy', + full_name='keypose.Camera.resy', + index=6, + number=7, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + ], + extensions=[], + nested_types=[], + enum_types=[], + 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enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='x', + full_name='keypose.Target.KeyPoint.x', + index=2, + number=3, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='y', + full_name='keypose.Target.KeyPoint.y', + index=3, + number=4, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='z', + full_name='keypose.Target.KeyPoint.z', + index=4, + number=5, + type=2, + cpp_type=6, + label=1, + has_default_value=False, + default_value=float(0), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='visible', + full_name='keypose.Target.KeyPoint.visible', + index=5, + number=6, + type=2, + cpp_type=6, + label=1, + has_default_value=True, + default_value=float(1), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + ], + extensions=[], + nested_types=[], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[], + serialized_start=285, + serialized_end=370, +) + +_TARGET = _descriptor.Descriptor( + name='Target', + full_name='keypose.Target', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='transform', + full_name='keypose.Target.transform', + index=0, + number=1, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='camera', + full_name='keypose.Target.camera', + index=1, + number=2, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='keypoints', + full_name='keypose.Target.keypoints', + index=2, + number=3, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + ], + extensions=[], + nested_types=[ + _TARGET_KEYPOINT, + ], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[], + serialized_start=158, + serialized_end=370, +) + +_KEYTARGETS = _descriptor.Descriptor( + name='KeyTargets', + full_name='keypose.KeyTargets', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='kp_target', + full_name='keypose.KeyTargets.kp_target', + index=0, + number=1, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='proj_targets', + full_name='keypose.KeyTargets.proj_targets', + index=1, + number=2, + type=11, + cpp_type=10, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='mirrored', + full_name='keypose.KeyTargets.mirrored', + index=2, + number=3, + type=8, + cpp_type=7, + label=1, + has_default_value=True, + default_value=False, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + ], + extensions=[], + nested_types=[], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[], + serialized_start=372, + serialized_end=484, +) + +_DATAPARAMS = _descriptor.Descriptor( + name='DataParams', + full_name='keypose.DataParams', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='resx', + full_name='keypose.DataParams.resx', + index=0, + number=1, + type=5, + cpp_type=1, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='resy', + full_name='keypose.DataParams.resy', + index=1, + number=2, + type=5, + cpp_type=1, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='num_kp', + full_name='keypose.DataParams.num_kp', + index=2, + number=3, + type=5, + cpp_type=1, + label=1, + has_default_value=False, + default_value=0, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='camera', + full_name='keypose.DataParams.camera', + index=3, + number=4, + type=11, + cpp_type=10, + label=1, + has_default_value=False, + default_value=None, + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + ], + extensions=[], + nested_types=[], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[], + serialized_start=486, + serialized_end=575, +) + +_TFSET = _descriptor.Descriptor( + name='TfSet', + full_name='keypose.TfSet', + filename=None, + file=DESCRIPTOR, + containing_type=None, + create_key=_descriptor._internal_create_key, + fields=[ + _descriptor.FieldDescriptor( + name='name', + full_name='keypose.TfSet.name', + index=0, + number=1, + type=9, + cpp_type=9, + label=1, + has_default_value=False, + default_value=b''.decode('utf-8'), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='common_dir', + full_name='keypose.TfSet.common_dir', + index=1, + number=2, + type=9, + cpp_type=9, + label=1, + has_default_value=False, + default_value=b''.decode('utf-8'), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='train', + full_name='keypose.TfSet.train', + index=2, + number=3, + type=9, + cpp_type=9, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='val', + full_name='keypose.TfSet.val', + index=3, + number=4, + type=9, + cpp_type=9, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='exclude', + full_name='keypose.TfSet.exclude', + index=4, + number=5, + type=9, + cpp_type=9, + label=3, + has_default_value=False, + default_value=[], + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + _descriptor.FieldDescriptor( + name='image_size', + full_name='keypose.TfSet.image_size', + index=5, + number=6, + type=9, + cpp_type=9, + label=1, + has_default_value=False, + default_value=b''.decode('utf-8'), + message_type=None, + enum_type=None, + containing_type=None, + is_extension=False, + extension_scope=None, + serialized_options=None, + file=DESCRIPTOR, + create_key=_descriptor._internal_create_key), + ], + extensions=[], + nested_types=[], + enum_types=[], + serialized_options=None, + is_extendable=False, + syntax='proto2', + extension_ranges=[], + oneofs=[], + serialized_start=577, + serialized_end=683, +) + +_TARGET_KEYPOINT.containing_type = _TARGET +_TARGET.fields_by_name['transform'].message_type = _TRANSFORM +_TARGET.fields_by_name['camera'].message_type = _CAMERA +_TARGET.fields_by_name['keypoints'].message_type = _TARGET_KEYPOINT +_KEYTARGETS.fields_by_name['kp_target'].message_type = _TARGET +_KEYTARGETS.fields_by_name['proj_targets'].message_type = _TARGET +_DATAPARAMS.fields_by_name['camera'].message_type = _CAMERA +DESCRIPTOR.message_types_by_name['Transform'] = _TRANSFORM +DESCRIPTOR.message_types_by_name['Camera'] = _CAMERA +DESCRIPTOR.message_types_by_name['Target'] = _TARGET +DESCRIPTOR.message_types_by_name['KeyTargets'] = _KEYTARGETS +DESCRIPTOR.message_types_by_name['DataParams'] = _DATAPARAMS +DESCRIPTOR.message_types_by_name['TfSet'] = _TFSET +_sym_db.RegisterFileDescriptor(DESCRIPTOR) + +Transform = _reflection.GeneratedProtocolMessageType( + 'Transform', + (_message.Message,), + { + 'DESCRIPTOR': _TRANSFORM, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.Transform) + }) +_sym_db.RegisterMessage(Transform) + +Camera = _reflection.GeneratedProtocolMessageType( + 'Camera', + (_message.Message,), + { + 'DESCRIPTOR': _CAMERA, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.Camera) + }) +_sym_db.RegisterMessage(Camera) + +Target = _reflection.GeneratedProtocolMessageType( + 'Target', + (_message.Message,), + { + 'KeyPoint': + _reflection.GeneratedProtocolMessageType( + 'KeyPoint', + (_message.Message,), + { + 'DESCRIPTOR': _TARGET_KEYPOINT, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.Target.KeyPoint) + }), + 'DESCRIPTOR': + _TARGET, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.Target) + }) +_sym_db.RegisterMessage(Target) +_sym_db.RegisterMessage(Target.KeyPoint) + +KeyTargets = _reflection.GeneratedProtocolMessageType( + 'KeyTargets', + (_message.Message,), + { + 'DESCRIPTOR': _KEYTARGETS, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.KeyTargets) + }) +_sym_db.RegisterMessage(KeyTargets) + +DataParams = _reflection.GeneratedProtocolMessageType( + 'DataParams', + (_message.Message,), + { + 'DESCRIPTOR': _DATAPARAMS, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.DataParams) + }) +_sym_db.RegisterMessage(DataParams) + +TfSet = _reflection.GeneratedProtocolMessageType( + 'TfSet', + (_message.Message,), + { + 'DESCRIPTOR': _TFSET, + '__module__': 'data_pb2' + # @@protoc_insertion_point(class_scope:keypose.TfSet) + }) +_sym_db.RegisterMessage(TfSet) + +# @@protoc_insertion_point(module_scope) diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/download_models.sh b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/download_models.sh new file mode 100644 index 000000000..88aee73f7 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/download_models.sh @@ -0,0 +1,18 @@ +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +#!/bin/bash + +wget https://storage.googleapis.com/keypose-transparent-object-dataset/models.zip +unzip -o models.zip -d keypose diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/estimator.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/estimator.py new file mode 100644 index 000000000..85fa1caf6 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/estimator.py @@ -0,0 +1,192 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Estimator definition using Keras and TF2. + +Adapted from 'Discovery of Latent 3D Keypoints via End-to-end +Geometric Reasoning' keypoint network. + +Given a 2D image and viewpoint, predict a set of 3D keypoints that +match the target examples. + +Can be instance or class specific, depending on training set. +""" + +import tensorflow as tf +from tensorflow import estimator as tf_estimator + +from keypose import losses as ls +from keypose import nets + + +def make_decay_function(step, lr_list): + """Decay function, stepwise linear from 0.1 to 0.01 of max lr. + + Args: + step: training step. + lr_list: [start_decay, end_decay, rate_0, rate_1, ...] + + Returns: + lr_func: decayed lr function. + lambda: 10-4 * lr_func() + """ + + start, end = lr_list[0], lr_list[1] + lr_seq = lr_list[2:] + seq_num = float(len(lr_seq) - 1) + seq_len = float(end - start) / seq_num + + def decayed_lr(): + if step < start: + return lr_seq[0] + elif step > end: + return lr_seq[-1] + else: + step_trunc = tf.cast(step - start, tf.float32) + num = tf.cast(step_trunc / seq_len, tf.int64) + lr_start = tf.gather(lr_seq, num) + lr_end = tf.gather(lr_seq, num + 1) + step_start = tf.cast(num, tf.float32) * seq_len + alpha = (step_trunc - step_start) / seq_len + return alpha * lr_end + (1.0 - alpha) * lr_start + + lr_func = tf.function(func=decayed_lr) + return lr_func, lambda: 1e-4 * lr_func() + + +def est_model_fn(features, labels, mode, params): + """Model function for the estimator. + + Args: + features: features from tfrecords. + labels: labels from tfrecords. + mode: train or eval. + params: ConfigParams for the model. + + Returns: + EstimatorSpec for the model. + """ + print('In est_model_fn') + + is_training = (mode == tf_estimator.ModeKeys.TRAIN) + + step = tf.compat.v1.train.get_or_create_global_step() + print('Step is:', step) + print('Mode is:', mode, is_training) + # print('labels is:', labels) + + mparams = params.model_params + model = nets.keypose_model(mparams, is_training) + # print('Features:\n', features) + preds = model(features, training=is_training) + print('Symbolic predictions:\n', preds) + + if mode == tf_estimator.ModeKeys.PREDICT: + print('Model_fn is returning from prediction mode') + return tf_estimator.EstimatorSpec(mode=mode, predictions=preds) + + # Get both the unconditional losses (the None part) + # and the input-conditional losses (the features part). + reg_losses = model.get_losses_for(None) + model.get_losses_for(features) + # Custom loss here. + print('Calling loss fn') + print('Reg losses:', reg_losses) + if reg_losses: + reg_loss = tf.math.add_n(reg_losses) + else: + reg_loss = 0.0 + print('Reg losses summed:', reg_loss) + + kp_loss, uvdw, xyzw = ls.keypose_loss(labels, preds, step, mparams) + total_loss = kp_loss + mparams.loss_reg * reg_loss + print('Total loss:', total_loss) + print('End calling loss fn') + + # Metrics from tf.keras.metrics, for eval only. + mae_disp_obj = tf.keras.metrics.Mean(name='MAE_disp') + mae_disp_obj.update_state(ls.disp_error(labels, uvdw, mparams)) + mae_uv_obj = tf.keras.metrics.Mean(name='MAE_uv') + mae_uv_obj.update_state(ls.uv_error(labels, uvdw, mparams)) + mae_world_obj = tf.keras.metrics.Mean(name='0_MAE_world') + mae_world_obj.update_state(ls.world_error(labels, xyzw)) + mae_2cm_obj = tf.keras.metrics.Mean(name='MAE_2cm') + mae_2cm_obj.update_state(ls.lt_2cm_error(labels, xyzw)) + metric_ops = { + 'MAE_disp': mae_disp_obj, + 'MAE_uv': mae_uv_obj, + 'MAE_2cm': mae_2cm_obj, + '0_MAE_world': mae_world_obj + } + + # Training stats - these correspond to the eval metrics. + # Don't know how to use v2 summaries here. + tf.compat.v1.summary.image( + 'viz_img_L', + ls.add_keypoints(features['img_L'], preds['uvd']), + max_outputs=4) + tf.compat.v1.summary.image( + 'viz_probs', + tf.expand_dims(tf.reduce_max(preds['prob'], axis=1), axis=-1), + max_outputs=4) + tf.compat.v1.summary.scalar( + 'MAE_disp', tf.reduce_mean(ls.disp_error(labels, uvdw, mparams))) + tf.compat.v1.summary.scalar( + 'MAE_uv', tf.reduce_mean(ls.uv_error(labels, uvdw, mparams))) + tf.compat.v1.summary.scalar('0_MAE_world', + tf.reduce_mean(ls.world_error(labels, xyzw))) + tf.compat.v1.summary.scalar('MAE_2cm', + tf.reduce_mean(ls.lt_2cm_error(labels, xyzw))) + # TODO(konolige): Add re-ordered prob loss back in. + # tf.compat.v1.summary.scalar('Prob_loss', + # tf.reduce_mean(ls.keypose_loss_prob(preds['prob'], labels))) + if is_training: + tf.compat.v1.summary.scalar( + 'Adjust_proj', ls.adjust_proj_factor(step, mparams.loss_proj_step)) + + train_op = None + if is_training: + # Using tf.keras.optimizers. + # NOTE: unlike in the V2 docs, lr_func is a zero-arg function that + # must access the optimizer step itself, rather than having it passed in. + # decay param in Adam is 1 / (1 + decay * step). Use custom decay. + lr_func, _ = make_decay_function(step, params.learning_rate) + + optimizer = tf.keras.optimizers.Adam( + learning_rate=lr_func, epsilon=1e-8, decay=0.0, clipnorm=5.0) + + # Manually assign tf.compat.v1.global_step variable to optimizer.iterations + # to make tf.compat.v1.train.global_step increase correctly. + # This assignment is a must for any `tf.train.SessionRunHook` specified in + # estimator, as SessionRunHooks rely on global step. + optimizer.iterations = step + # Get both the unconditional updates (the None part) + # and the input-conditional updates (the features part). + update_ops = model.get_updates_for(None) + model.get_updates_for(features) + # Compute the minimize_op. + + print(tf.compat.v1.global_variables(scope='batch')) + train_vars = model.trainable_variables + print('Trainable vars:\n', train_vars) + print('Trainable flags:\n', [(x.name, x.trainable) for x in train_vars]) + minimize_op = optimizer.get_updates(total_loss, train_vars)[0] + train_op = tf.group(minimize_op, *update_ops) + tf.compat.v1.summary.scalar('Learning_rate', optimizer.lr) + + return tf_estimator.EstimatorSpec( + mode=mode, + predictions=preds, + loss=total_loss, + train_op=train_op, + eval_metric_ops=metric_ops) diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/gen_tfrecords.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/gen_tfrecords.py new file mode 100644 index 000000000..22873fc2b --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/gen_tfrecords.py @@ -0,0 +1,317 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""A script to generate a tfrecord file from a folder containing the renderings. + +See data.proto for protobuf definitions. + +Format of the files in the folder: +1. Stereo images, with filenames ######_L/R.png + - Format is RGBA, where the alpha channel indicates the object mask, if + available. + - Right images may be missing, for non-stereo data. + +2. Camera / object transform and keypoints for each stereo pair in + "######_L/R.pbtxt" + - Protobuf (in text format) containing the transform, camera, and kps + - 4x4 Transform takes 3D points in homogeneous form from the object to the + camera frame. + - Camera parameters containing focal length, center, + stereo displacement. + - Cameras are assumed to be rectified and aligned, with the same camera + matrix. + - Keypoints are ordered, and all files should have the same number of + keypoints. If a keypoint is to be skipped, it should be [-1, -1] + +4. Target image camera / object transform and keypoints in files + "target###.pbtxt" + +Example usage (from directory above keypose/): + python3 -m keypose.gen_tfrecords configs/tfset_bottle_0_t5 +""" +import glob +import os +import shutil +import sys + +import numpy as np +import tensorflow as tf + +from keypose import utils + + +def bytes_feature(values): + return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values])) + + +def generate_tfrecords(image_dirs, resize_str, params, num_samples_per_rec=20): + """Generate tfrecords in the image directories. + + tfrecords are of the form NNNN.tfrecord, in a tfrecord/ subdir. + Images are resized according to params. + + Args: + image_dirs: directories with image files. + resize_str: subdir based on image resizing, e.g., 756x424_fl400. + params: DataParams for the image size and camera. + num_samples_per_rec: number of samples per tfrecord. + """ + print('Image folders:', image_dirs) + + # Iterate over image folders, processing each. + cam = params.camera + for image_dir in image_dirs: + print('Processing folder %s' % image_dir) + # Get data params, set new ones. + _, _, num_kp, cam_orig = utils.read_data_params( + os.path.join(image_dir, 'data_params.pbtxt')) + cam.baseline = cam_orig.baseline + params.num_kp = num_kp + out_dir = os.path.join(image_dir, resize_str) + os.makedirs(out_dir, exist_ok=True) + utils.write_to_text_file(os.path.join(out_dir, 'data_params.pbtxt'), params) + + # Read image files and generate tfrecords. + filenames_left = glob.glob(os.path.join(image_dir, '*_L.png')) + filenames_right = glob.glob(os.path.join(image_dir, '*_R.png')) + filenames_target_left = glob.glob(os.path.join(image_dir, '*_L.pbtxt')) + filenames_target_right = glob.glob(os.path.join(image_dir, '*_R.pbtxt')) + if not filenames_right: + filenames_right = filenames_left + filenames_target_right = filenames_target_left + else: + print('Have stereo images') + assert len(filenames_left) == len(filenames_target_left) + assert len(filenames_target_left) == len(filenames_target_right) + assert len(filenames_left) == len(filenames_right) + filenames_left.sort() + filenames_right.sort() + filenames_target_left.sort() + filenames_target_right.sort() + total = len(filenames_left) + print('Found %d total image examples' % total) + + j = 0 + while j < total: + jj = min(total, j + num_samples_per_rec) + record_name = ('%04d' % j) + '.tfrecord' + print('Record file %s' % record_name) + path = os.path.join(out_dir, record_name) + print('Generating examples %d to %d' % (j, jj)) + generate(path, cam_orig, cam, filenames_left[j:jj], filenames_right[j:jj], + filenames_target_left[j:jj], filenames_target_right[j:jj]) + j = jj + + +def generate(path, cam_orig, cam_new, fnames_left, fnames_right, + fnames_target_left, fnames_target_right): + """Generate tfrecords for a set of images and their metadata.""" + with tf.io.TFRecordWriter(path) as tfrecord_writer: + with tf.Graph().as_default(): + im0 = tf.compat.v1.placeholder(dtype=tf.uint8) + im1 = tf.compat.v1.placeholder(dtype=tf.uint8) + encoded0 = tf.image.encode_png(im0) + encoded1 = tf.image.encode_png(im1) + + with tf.compat.v1.Session() as sess: + for fleft, fright, ftleft, ftright in zip(fnames_left, fnames_right, + fnames_target_left, + fnames_target_right): + assert (os.path.basename( + os.path.splitext(fleft)[0]) == os.path.basename( + os.path.splitext(ftleft)[0])) + assert (os.path.basename( + os.path.splitext(fright)[0]) == os.path.basename( + os.path.splitext(ftright)[0])) + print(fleft) + image_left = utils.read_image(fleft) + image_right = utils.read_image(fright) + targs_left = utils.read_target_pb(ftleft) + targs_right = utils.read_target_pb(ftright) + image_left = utils.resize_image(image_left, cam_new, cam_orig, + targs_left) + image_right = utils.resize_image(image_right, cam_new, cam_orig, + targs_right) + st0, st1 = sess.run([encoded0, encoded1], + feed_dict={ + im0: image_left, + im1: image_right + }) + feats = {'img_L': bytes_feature(st0), 'img_R': bytes_feature(st1)} + feats.update(make_features(targs_left, 'L')) + feats.update(make_features(targs_right, 'R')) + example = tf.train.Example(features=tf.train.Features(feature=feats)) + tfrecord_writer.write(example.SerializeToString()) + + +def make_features(targs_pb, pf): + """Make tfrecord featurs from image metadata.""" + camera, to_uvd, to_world, keys_uvd, _, visible, _ = utils.get_contents_pb( + targs_pb.kp_target) + num_kp = len(keys_uvd) + # Restrict to max projection targets + proj_targs = [ + utils.get_contents_pb(targ_pb) for targ_pb in targs_pb.proj_targets + ][:utils.MAX_TARGET_FRAMES] + targets_keys_uvd = [] + targets_to_uvd = [] + for proj_targ in proj_targs: + _, to_uvd, _, keys_uvd, _, _, _ = proj_targ + targets_keys_uvd.append(keys_uvd) + targets_to_uvd.append(to_uvd) + # Add dummy targets if necessary. + num_targets = len(proj_targs) + for _ in range(utils.MAX_TARGET_FRAMES - num_targets): + targets_keys_uvd.append(utils.dummy_keys_uvd(num_kp)) + targets_to_uvd.append(utils.dummy_to_uvd()) + + def feat_int(num): + return tf.train.Feature(int64_list=tf.train.Int64List(value=[num])) + + def feat_floats(floats): + return tf.train.Feature(float_list=tf.train.FloatList(value=floats)) + + feats = { + 'to_world_' + pf: + feat_floats(to_world.flatten()), + 'to_uvd_' + pf: + feat_floats(to_uvd.flatten()), + 'camera_' + pf: + feat_floats(utils.cam_pb_to_array(camera)), + 'keys_uvd_' + pf: + feat_floats(np.array(keys_uvd).flatten()), + 'visible_' + pf: + feat_floats(visible), + 'num_kp_' + pf: + feat_int(num_kp), + 'num_targets_' + pf: + feat_int(num_targets), + 'targets_to_uvd_' + pf: + feat_floats(np.array(targets_to_uvd).flatten()), + 'targets_keys_uvd_' + pf: + feat_floats(np.array(targets_keys_uvd).flatten()), + 'mirrored': + feat_int(int(targs_pb.mirrored)), + } + return feats + + +def group_tfrecords(train_dirs, val_dirs, tfset, data_params): + """Group all relevant tfrecords into a new tfrecord/ directory.""" + out_dir = os.path.join(utils.KEYPOSE_PATH, + os.path.join('tfrecords', tfset.name)) + records_out_dir = os.path.join(out_dir, 'tfrecords') + os.makedirs(records_out_dir, exist_ok=True) + + start = 0 + train_names = [] + for dr in train_dirs: + tfrecord_dir = os.path.join(dr, tfset.image_size) + for tfname in glob.glob(os.path.join(tfrecord_dir, '*.tfrecord')): + rname = ('%04d' % start) + '.tfrecord' + fname = os.path.join(records_out_dir, rname) + start += 1 + print('Copying %s to %s' % (tfname, fname)) + shutil.copy(tfname, fname) + train_names.append(rname) + + val_names = [] + for dr in val_dirs: + tfrecord_dir = os.path.join(dr, tfset.image_size) + for tfname in glob.glob(os.path.join(tfrecord_dir, '*.tfrecord')): + rname = ('%04d' % start) + '.tfrecord' + fname = os.path.join(records_out_dir, rname) + start += 1 + print('Copying %s to %s' % (tfname, fname)) + shutil.copy(tfname, fname) + val_names.append(rname) + + tfset_pb = utils.make_tfset(train_names, val_names, tfset.name) + utils.write_to_text_file( + os.path.join(records_out_dir, 'tfset.pbtxt'), tfset_pb) + utils.write_to_text_file( + os.path.join(records_out_dir, 'data_params.pbtxt'), data_params) + utils.write_to_text_file(os.path.join(out_dir, 'tfset_def.pbtxt'), tfset) + + +# Exclude directories from a list. +def exclude_dirs(dirs, exclude): + return [dir for dir in dirs if dir not in exclude] + + +def main(argv): + if not len(argv) >= 2: + print( + 'Usage: ./trainer.py [tfset_only]' + ) + exit(0) + + config_file = argv[1] + tfset_only = False + if len(argv) > 2: + if argv[2] == 'tfset_only': + tfset_only = True + else: + print('Second argument must be tfset_only') + exit(0) + + # Check for tfset file. + fname = os.path.join(utils.KEYPOSE_PATH, config_file + '.pbtxt') + tfset = utils.read_tfset(fname) + common_dir = tfset.common_dir + print('common dir:', common_dir) + print('train:', tfset.train) + print('val:', tfset.val) + print('exclude:', tfset.exclude) + + common_dir = os.path.join(utils.KEYPOSE_PATH, common_dir) + # pylint: disable=g-complex-comprehension + train_dirs = [ + dr for tfdir in tfset.train + for dr in glob.glob(os.path.join(common_dir, tfdir)) + ] + val_dirs = [ + dr for tfdir in tfset.val + for dr in glob.glob(os.path.join(common_dir, tfdir)) + ] + exc_dirs = [ + dr for tfdir in tfset.exclude + for dr in glob.glob(os.path.join(common_dir, tfdir)) + ] + train_dirs = exclude_dirs(train_dirs, exc_dirs) + val_dirs = exclude_dirs(val_dirs, exc_dirs) + train_dirs = exclude_dirs(train_dirs, val_dirs) + train_dirs.sort() + val_dirs.sort() + all_dirs = train_dirs + val_dirs + all_dirs.sort() + + # Set up data_params for the new image size. + size, focal_length = tfset.image_size.split('_') + resx, resy = size.split('x') + resx = int(resx) + resy = int(resy) + fx = int(focal_length[2:]) + cam = utils.cam_array_to_pb([fx, fx, resx / 2, resy / 2, 0.0, resx, resy]) + data_params = utils.make_data_params(resx, resy, 0, cam) + + if not tfset_only: + generate_tfrecords(all_dirs, tfset.image_size, data_params) + + group_tfrecords(train_dirs, val_dirs, tfset, data_params) + + +if __name__ == '__main__': + main(sys.argv) diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/inputs.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/inputs.py new file mode 100644 index 000000000..e410db2fa --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/inputs.py @@ -0,0 +1,458 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Dataset input functions for KeyPose estimator.""" + +import glob +import os + +import numpy as np +import tensorflow as tf + +from keypose import utils + + +def get_tfrecords(dset_dir, split='train'): + """Get tfrecords that match training, validation, and test sets. + + Works off the TfSet protobuf. + + Args: + dset_dir: dataset directory of tfrecords. + split: train or eval. + + Returns: + Relevant tfrecords from dset_dir. + + Raises: + IOError: if tfset.pbtxt protobuf file not found. + """ + tfset_fname = os.path.join(dset_dir, 'tfset.pbtxt') + if not os.path.isfile(tfset_fname): + raise IOError('%s not found' % tfset_fname) + tfset_pb = utils.read_tfset(tfset_fname) + + fnames = [] + if split == 'train': + fnames = tfset_pb.train + elif split == 'val': + fnames = tfset_pb.val + elif split == 'test': + fnames = tfset_pb.test + elif split == 'all': + fnames = glob.glob(os.path.join(dset_dir, '*.tfrecord')) + print('Found %d tfrecord files' % len(fnames)) + return fnames + + return [os.path.join(dset_dir, f) for f in fnames] + + +# Return True if all kps are visible. +def filter_nonvisible(_, labels): + """Check visibility of keypoints. + + Args: + labels: labels of the tfrecord. + + Returns: + True if all keypoints are visible. + """ + visible = labels['visible_L'] + isallowed = tf.equal(visible, tf.constant(0.)) + reduced = tf.reduce_sum(tf.cast(isallowed, tf.float32)) + visible_r = labels['visible_R'] + isallowed_r = tf.equal(visible_r, tf.constant(0.)) + reduced_r = tf.reduce_sum(tf.cast(isallowed_r, tf.float32)) + return tf.equal(tf.add(reduced, reduced_r), tf.constant(0.)) + + +def permute_order(tensor, order, mask, is_target=False): + """Reduces of shape [order, ...] by picking out order dim. + + Args: + tensor: input tensor, with orderings. + order: order permutation. + mask: which order to use. + is_target: True if tensor is a target (???). + + Returns: + Reduced tensor. + """ + if is_target: + tensor = tf.stack( + # order is list of lists, so pylint: disable=not-an-iterable + [tf.stack([tensor[:, x, Ellipsis] for x in ord], axis=1) for ord in order], + axis=1) + res = tf.multiply(tensor, mask) + return tf.reduce_sum(res, axis=1) + else: + tensor = tf.stack( + # order is list of lists, so pylint: disable=not-an-iterable + [tf.stack([tensor[x, Ellipsis] for x in ord], axis=0) for ord in order], + axis=0) + res = tf.multiply(tensor, mask) + return tf.reduce_sum(res, axis=0) + + +def parser(serialized_example, resx, resy, num_kp, sym=None, return_float=True): + """Parses a single tf.Example into image and label tensors. + + Args: + serialized_example: tfrecord sample. + resx: image x resolution, pixels. + resy: image y resolution, pixles. + num_kp: number of keypoints. + sym: list representing keypoint symmetry. + return_float: True if we want a floating-point return. + + Returns: + Feature set. + """ + if sym is None: + sym = [0] + fs = tf.io.parse_single_example( + serialized_example, + features={ + 'img_L': + tf.io.FixedLenFeature([], tf.string), + 'img_R': + tf.io.FixedLenFeature([], tf.string), + 'to_world_L': + tf.io.FixedLenFeature([4, 4], tf.float32), + 'to_world_R': + tf.io.FixedLenFeature([4, 4], tf.float32), + 'to_uvd_L': + tf.io.FixedLenFeature([4, 4], tf.float32), + 'to_uvd_R': + tf.io.FixedLenFeature([4, 4], tf.float32), + 'camera_L': + tf.io.FixedLenFeature([7], tf.float32), + 'camera_R': + tf.io.FixedLenFeature([7], tf.float32), + 'keys_uvd_L': + tf.io.FixedLenFeature([num_kp, 4], tf.float32), + 'keys_uvd_R': + tf.io.FixedLenFeature([num_kp, 4], tf.float32), + 'visible_L': + tf.io.FixedLenFeature([num_kp], tf.float32), + 'visible_R': + tf.io.FixedLenFeature([num_kp], tf.float32), + 'num_kp_L': + tf.io.FixedLenFeature([], tf.int64), + 'num_kp_R': + tf.io.FixedLenFeature([], tf.int64), + 'num_targets_L': + tf.io.FixedLenFeature([], tf.int64), + 'num_targets_R': + tf.io.FixedLenFeature([], tf.int64), + 'mirrored': + tf.io.FixedLenFeature([], tf.int64), + 'targets_to_uvd_L': + tf.io.FixedLenFeature([utils.MAX_TARGET_FRAMES, 4, 4], + tf.float32), + 'targets_to_uvd_R': + tf.io.FixedLenFeature([utils.MAX_TARGET_FRAMES, 4, 4], + tf.float32), + 'targets_keys_uvd_L': + tf.io.FixedLenFeature([utils.MAX_TARGET_FRAMES, num_kp, 4], + tf.float32), + 'targets_keys_uvd_R': + tf.io.FixedLenFeature([utils.MAX_TARGET_FRAMES, num_kp, 4], + tf.float32), + }) + + fs['img_L'] = tf.image.decode_png(fs['img_L'], 4) + if return_float: + fs['img_L'] = tf.image.convert_image_dtype(fs['img_L'], tf.float32) + fs['img_R'] = tf.image.decode_png(fs['img_R'], 4) + if return_float: + fs['img_R'] = tf.image.convert_image_dtype(fs['img_R'], tf.float32) + + fs['img_L'].set_shape([resy, resx, 4]) + fs['img_R'].set_shape([resy, resx, 4]) + + # Check for randomizing keypoint symmetry order. + if len(sym) > 1: + order = utils.make_order(sym, num_kp) + mask = [0.0 for x in sym] + mask[0] = 1.0 + mask = tf.random.shuffle(mask) # [order] + mask2 = tf.expand_dims(mask, -1) # [order, 1] + mask2 = tf.expand_dims(mask2, -1) # [order, 1, 1] + mask3 = tf.stack( + [mask2] * utils.MAX_TARGET_FRAMES, axis=0) # [targets, order, 1, 1] + fs['keys_uvd_L'] = permute_order(fs['keys_uvd_L'], order, mask2) + fs['keys_uvd_R'] = permute_order(fs['keys_uvd_R'], order, mask2) + fs['targets_keys_uvd_L'] = permute_order(fs['targets_keys_uvd_L'], order, + mask3, True) + fs['targets_keys_uvd_R'] = permute_order(fs['targets_keys_uvd_R'], order, + mask3, True) + print('Targets to_uvd shape [num_targs, 4, 4]:', fs['targets_to_uvd_L'].shape) + print('Targets keys_uvd shape [num_targs, num_kp, 4]:', + fs['targets_keys_uvd_L'].shape) + + return fs + + +def create_input_fn(params, + split='val', + keep_order=False, + filenames=None, + late_fusion=False): + """Returns input_fn for tf.estimator.Estimator. + + Reads tfrecords and constructs input_fn for either training or eval. All + tfrecords not in val.txt will be assigned to training set. + train.txt and val.txt have the base filenames for their tfrecords, one per + line. + + Args: + params: ConfigParams structure. There are various elements used -- + keep_order - True if order of images should be preserved. dset_dir - + Directory containing tfrecord data of original images. batch_size - The + batch size! occ_fraction - Fraction of object to occlude. noise - Random + noise to add to an image, in pixel values; (a,b) is bounds. blur - Random + gaussian blur to add, sigma in pixels (low, high). motion - Random motion + blur to add (move_pixels_min, move_pixels_max, angle_deg_min, + angle_deg_max). rot - (rotx, roty, rotz) for camera in degrees. gamma - + Gamma value to modify image; (a,b) is bounds. num_kp - Number of + keypoints. + resx, resy - Size of input images. NB: using 'crop' causes output size to + change. crop - Crop image and offset right crop, e.g., [280, 180, 20]. + sym - keypoint symmetry list, [0] is no symmetry. kp_occ_radius - Radius + (m) around a keypoint for occlusion. + split: A string indicating the split. Can be either 'train' or 'val'. + keep_order: keep tfrecord order, for prediction of a sequence. + filenames: set of tfrecord filenames. + late_fusion: True if left / right images are handled separately by the net. + + Returns: + input_fn for tf.estimator.Estimator. + + Raises: + IOError: If val.txt is not found. + """ + + print('Input function called for split <%s>' % split) + + mparams = params.model_params + dset_dir = params.dset_dir + batch_size = params.batch_size + occ_fraction = mparams.occ_fraction + noise = mparams.noise + blur = mparams.blur + motion = mparams.motion + rotation = mparams.rot + gamma = mparams.gamma + num_kp = mparams.num_kp + resx = mparams.resx + resy = mparams.resy + crop = mparams.crop + shear = mparams.shear + scale = mparams.scale + flip = mparams.flip + sym = mparams.sym + input_sym = mparams.input_sym + dither = mparams.dither + visible_only = mparams.visible + + if not filenames: + filenames = get_tfrecords(dset_dir, split) + if not filenames: + raise IOError('No tfrecord files found!') + print('Found %d files' % len(filenames)) + bg_filenames = [''] # No filename, just use a dummy image. + + def input_fn(): + """input_fn for tf.estimator.Estimator.""" + + def zip_parser(sample, bg_filename): + """Modifies the input training images. + + Args: + sample: A dict of features from the TFRecord files. + bg_filename: A string, filename of a background scene. + + Returns: + A sample with the images modified by image augmentation. + """ + image = sample['img_L'] + image2 = sample['img_R'] + rgba_shape = image.get_shape() + final_shape = rgba_shape + to_world = sample['to_world_L'] + to_world = tf.reshape(to_world, [4, 4]) + keys_uvd = sample['keys_uvd_L'] + keys_uvd = tf.reshape(keys_uvd, [-1, 4]) + visible = sample['visible_L'] + keys_uvd_r = sample['keys_uvd_R'] + keys_uvd_r = tf.reshape(keys_uvd_r, [-1, 4]) + camera = sample['camera_L'] + mirrored = sample['mirrored'] + # uvd offset based on cropping. + sample['offsets'] = tf.constant([0.0, 0.0, 0.0]) + + # Causes issues for rotation (keypoint changes). + if split == 'train' and (rotation[0] or rotation[1] or rotation[2]): + # Random rotation around camera center. + + image, image2, transform, keys_uvd, visible = tf.numpy_function( + func=utils.do_rotation, + inp=[image, image2, to_world, camera, keys_uvd, visible, rotation], + Tout=[tf.float32, tf.float32, tf.float32, tf.float32, tf.float32]) + image.set_shape(rgba_shape) + image2.set_shape(rgba_shape) + transform.set_shape(to_world.get_shape()) + keys_uvd.set_shape(keys_uvd.get_shape()) + visible.set_shape(sample['visible_L'].get_shape()) + sample['to_world_L'] = transform + sample['keys_uvd_L'] = keys_uvd + else: + image = utils.image_uint8_to_float(image) + image2 = utils.image_uint8_to_float(image2) + + final_shape = [crop[1], crop[0], 4] + # Will filter if visible returns a 0. + image, image2, offsets, visible = tf.numpy_function( + func=utils.do_occlude_crop, + inp=[ + image, image2, keys_uvd, keys_uvd_r, crop, visible, dither, + late_fusion + ], + Tout=[tf.float32, tf.float32, tf.float32, tf.float32]) + sample['offsets'] = offsets + sample['visible_L'] = visible + image.set_shape(final_shape) + image2.set_shape(final_shape) + if occ_fraction and split == 'train': + image, image2 = tf.numpy_function( + func=utils.do_occlude, + inp=[image, image2, occ_fraction], + Tout=[tf.float32, tf.float32]) + image.set_shape(final_shape) + image2.set_shape(final_shape) + + # Check for mirrored sample. + image, image2, hom = tf.numpy_function( + func=utils.do_vertical_flip, + inp=[image, image2, mirrored], + Tout=[tf.float32, tf.float32, tf.float32]) + image.set_shape(final_shape) + image2.set_shape(final_shape) + hom.set_shape((3, 3)) + + # 2D geometric transform. + image, image2, hom = tf.numpy_function( + func=utils.do_2d_homography, + inp=[image, image2, scale, shear, flip, mirrored, split], + Tout=[tf.float32, tf.float32, tf.float32]) + image.set_shape(final_shape) + image2.set_shape(final_shape) + hom.set_shape((3, 3)) + + sample['hom'] = hom + + # Compute ground truth spatial probabilities. + # TODO(konolige): Make variance a parameter. + probs = tf.numpy_function( + func=utils.do_spatial_prob, + inp=[keys_uvd, hom, offsets, 20.0, crop[:2]], + Tout=tf.float32) + probs.set_shape([num_kp, final_shape[0], final_shape[1]]) + sample['prob_label'] = probs + + # Composite with background. + image = tf.numpy_function( + func=utils.do_composite, + inp=[image, '', blur, motion, (0.0, noise), + gamma], # Use gray background. + Tout=tf.float32) + # Shape is [h, w, 3] + image.set_shape((final_shape[0], final_shape[1], 3)) + image2 = tf.numpy_function( + func=utils.do_composite, + inp=[image2, bg_filename, blur, motion, (0.0, noise), gamma], + Tout=tf.float32) + # Shape is [h, w, 3] + image2.set_shape((final_shape[0], final_shape[1], 3)) + + if split == 'train': + # Introduce photometric randomness for generalization. + brightness = 32.0 + contrast = (0.7, 1.2) + image = tf.image.random_hue(image, max_delta=0.1) + image = tf.image.random_saturation(image, 0.6, 1.2) + image = tf.image.random_contrast(image, contrast[0], contrast[1]) + image = tf.image.random_brightness(image, max_delta=brightness / 255.0) + image = tf.clip_by_value(image, 0.0, 1.0) + image2 = tf.image.random_hue(image2, max_delta=0.1) + image2 = tf.image.random_saturation(image2, 0.6, 1.2) + image2 = tf.image.random_contrast(image2, contrast[0], contrast[1]) + image2 = tf.image.random_brightness( + image2, max_delta=brightness / 255.0) + image2 = tf.clip_by_value(image2, 0.0, 1.0) + + features = {} + features['img_L'] = tf.image.convert_image_dtype(image, tf.float32) + features['img_R'] = tf.image.convert_image_dtype(image2, tf.float32) + features['offsets'] = sample['offsets'] + features['hom'] = sample['hom'] + features['to_world_L'] = sample['to_world_L'] + del sample['img_L'] + del sample['img_R'] + return features, sample # That is, (features, labels) for Estimator. + + # Main code of create_input_fn here. + pcount = 8 + rcount = pcount + if split == 'train': + np.random.shuffle(filenames) + else: + filenames.sort() + + np.random.shuffle(bg_filenames) + bg_dataset = tf.data.Dataset.from_tensor_slices(bg_filenames) + bg_dataset = bg_dataset.shuffle(200).repeat() + + if keep_order: + rcount = 1 + dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=rcount) + parse_sym = sym + if len(input_sym) > 1: + parse_sym = input_sym # Mix up keypoints on input. + + def parse_it(x): + return parser(x, resx, resy, num_kp, sym=parse_sym, return_float=True) + + dataset = dataset.map(parse_it, num_parallel_calls=pcount) + + if split == 'train': + dataset = dataset.shuffle(1200) + if not keep_order: + dataset = dataset.repeat() + elif batch_size > 1 and keep_order: + dataset = dataset.flat_map( + lambda x: tf.data.Dataset.from_tensors(x).repeat(batch_size)) + + # Add a random background and composite it in. + dataset = tf.data.Dataset.zip((dataset, bg_dataset)) + dataset = dataset.map(zip_parser, num_parallel_calls=pcount) + if visible_only: + dataset = dataset.filter(filter_nonvisible) + dataset = dataset.batch(batch_size, drop_remainder=True).prefetch(buffer_size=batch_size) + + return dataset + + return input_fn diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/losses.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/losses.py new file mode 100644 index 000000000..ad8e3bc28 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/losses.py @@ -0,0 +1,367 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Loss and metric definitions for KeyPose models.""" + +import numpy as np +import tensorflow as tf + +from keypose import nets + +# num_targs = inp.MAX_TARGET_FRAMES +num_targs = 5 + +# Reordering based on symmetry. + + +def make_order(sym, num_kp): + """Returns all rotations of the keypoints.""" + rots = np.array([sym[-i:] + sym[:-i] for i in range(len(sym))]) + rot_list = np.array([range(num_kp)] * len(sym)) + for i, rot in enumerate(rot_list): + rot[rots[i]] = rot_list[0][rots[0]] + return rot_list + + +def reorder(tensor, order): + """Re-orders a tensor along the num_kp dimension. + + Args: + tensor: has shape [batch, num_kp, ...] + order: permutation of keypoints. + + Returns: + shape [batch, order, num_kp, ...] + """ + return tf.stack( + # order is list of lists, so pylint: disable=not-an-iterable + [tf.stack([tensor[:, x, ...] for x in ord], axis=1) for ord in order], + axis=1) + + +def reduce_order(tensor, mask, mean=False): + res = tf.multiply(tensor, mask) + res = tf.reduce_sum(res, axis=1) + if mean: + return tf.reduce_mean(res) + else: + return res + + +# Loss functions. + + +def project(tmat, tvec, tvec_transpose=False): + """Projects homogeneous 3D XYZ coordinates to image uvd coordinates, or vv. + + Args: + tmat: has shape [[N,] batch_size, 4, 4]. + tvec: has shape [[N,] batch_size, 4, num_kp] or [batch_size, num_kp, 4]. + tvec_transpose: True if tvec is to be transposed before application. + + Returns: + Has shape [[N,] batch_size, 4, num_kp]. + """ + tp = tf.matmul(tmat, tvec, transpose_b=tvec_transpose) + # Using <3:4> instead of <3> preserves shape. + tp = tp / (tp[..., 3:4, :] + 1.0e-10) + return tp + + +def keypoint_loss_targets(uvd, keys_uvd, mparams): + """Computes the supervised keypoint loss between computed and gt keypoints. + + Args: + uvd: [batch, order, num_targs, 4, num_kp] Predicted set of keypoint uv's + (pixels). + keys_uvd: [batch, order, num_targs, 4, num_kp] The ground-truth set of uvdw + coords. + mparams: model parameters. + + Returns: + Keypoint projection loss of size [batch, order]. + """ + print('uvd shape in klt [batch, order, num_targs, 4, num_kp]:', uvd.shape) + print('keys_uvd shape in klt [batch, order, num_targs, 4, num_kp]:', + keys_uvd.shape) + keys_uvd = nets.to_norm(keys_uvd, mparams) + uvd = nets.to_norm(uvd, mparams) + + wd = tf.square(uvd[..., :2, :] - keys_uvd[..., :2, :]) + wd = tf.reduce_sum(wd, axis=[-1, -2]) # uv dist [batch, order, num_targs] + print('wd shape in klt [batch, order, num_targs]:', wd.shape) + wd = tf.reduce_mean(wd, axis=[-1]) # [batch, order] + return wd + + +# Compute the reprojection error on the target frames. +def keypose_loss_proj(uvdw_pos, labels, mparams, num_order): + """Compute the reprojection error on the target frames. + + Args: + uvdw_pos: predicted uvd, always positive. + labels: sample labels. + mparams: model parameters. + num_order: number of order permutations. + + Returns: + Scalar loss. + """ + num_kp = mparams.num_kp + to_world = labels['to_world_L'] # [batch, 4, 4] + to_world_order = tf.stack( + [to_world] * num_order, axis=1) # [batch, order, 4, 4] + to_world_order = tf.ensure_shape( + to_world_order, [None, num_order, 4, 4], name='to_world_order') + world_coords = project(to_world_order, uvdw_pos, + True) # [batch, order, 4, num_kp] + world_coords = tf.ensure_shape( + world_coords, [None, num_order, 4, num_kp], name='world_coords') + print('world_coords shape [batch, order, 4, num_kp]:', world_coords.shape) + + # Target transform and keypoints. + # [batch, num_targs, 4, 4] for transforms + # [batch, num_targs, 4, num_kp] for keypoints (after transpose) + targets_to_uvd = labels['targets_to_uvd_L'] + targets_keys_uvd = tf.transpose(labels['targets_keys_uvd_L'], [0, 1, 3, 2]) + targets_keys_uvd_order = tf.stack([targets_keys_uvd] * num_order, axis=1) + print('Model fn targets_to_uvd shape [batch, num_targs, 4, 4]:', + targets_to_uvd.shape) + print( + 'Model fn targets_keys_uvd_order shape [batch, order, num_targs, 4, ' + 'num_kp]:', targets_keys_uvd_order.shape) + + # [batch, order, num_targs, 4, num_kp] + proj_uvds = project( + tf.stack([targets_to_uvd] * num_order, axis=1), + tf.stack([world_coords] * num_targs, axis=2)) + proj_uvds = tf.ensure_shape( + proj_uvds, [None, num_order, 5, 4, num_kp], name='proj_uvds') + print('proj_uvds shape [batch, order, num_targs, 4, num_kp]:', + proj_uvds.shape) + loss_proj = keypoint_loss_targets(proj_uvds, targets_keys_uvd_order, mparams) + loss_proj = tf.ensure_shape(loss_proj, [None, num_order], name='loss_proj') + print('loss_proj shape [batch, order]:', loss_proj.shape) + return loss_proj + + +# Keypoint loss function, direct comparison of u,v,d. +# Compares them in normalized image coordinates. For some reason this +# seems to work better than pixels. +# uvdw_order: [batch, order, num_kp, 4] +# keys_uvd_order: [batch, order, num_kp, 4] +# Returns: [batch, order] +def keypose_loss_kp(uvdw_order, keys_uvd_order, mparams): + uvdw_order = nets.to_norm_vec(uvdw_order, mparams) + keys_uvd_order = nets.to_norm_vec(keys_uvd_order, mparams) + ret = tf.reduce_sum( + tf.square(uvdw_order[..., :3] - keys_uvd_order[..., :3]), + axis=[-1, -2]) # [batch, order] + return ret + + +# Probability coherence loss. +def keypose_loss_prob(prob_order, prob_label_order): + ret = tf.reduce_sum( + prob_order * prob_label_order, axis=[-1, -2]) # [batch, order] + return tf.reduce_mean(ret, axis=-1) + + +# Adjust the gain of loss_proj; return in [0,1]. +def adjust_proj_factor(step, loss_step, minf=0.0): + if loss_step[1] == 0: + return 1.0 + step = tf.cast(step, tf.float32) + return tf.maximum( + minf, + tf.minimum((step - loss_step[0]) / + tf.cast(loss_step[1] - loss_step[0], tf.float32), 1.0)) + + +# Custom loss function for the Keras model in Estimator +# Args are: +# Dict of tensors for labels, with keys_uvd, offsets. +# Tensor of raw uvd values for preds, [batch, num_kp, 3], +# order is [u,v,d]. +# Note that the loss is batched. +# This loss only works with tf Estimator, not keras models. +@tf.function +def keypose_loss(labels, preds, step, mparams, do_print=True): + """Custom loss function for the Keras model in Estimator. + + Note that the loss is batched. + This loss only works with tf Estimator, not keras models. + + Args: + labels: dict of tensors for labels, with keys_uvd, offsets. + preds: tensor of raw uvd values for preds, [batch, num_kp, 3], order is + [u,v,d]. + step: training step. + mparams: model training parameters. + do_print: True to print loss values at every step. + + Returns: + Scalar loss. + """ + num_kp = mparams.num_kp + sym = mparams.sym + order = make_order(sym, num_kp) + num_order = len(order) + + uvdw = preds['uvdw'] + uvdw_pos = preds['uvdw_pos'] + uv_pix_raw = preds['uv_pix_raw'] + prob = preds['prob'] # [batch, num_kp, resy, resx] + xyzw = tf.transpose(preds['xyzw'], [0, 2, 1]) # [batch, num_kp, 4] + + uvdw_order = reorder(uvdw, order) # [batch, order, num_kp, 4] + print('uvdw_order shape:', uvdw_order.shape) + uvdw_pos_order = reorder(uvdw_pos, order) # [batch, order, num_kp, 4] + xyzw_order = reorder(xyzw, order) # [batch, order, 4, num_kp] + print('xyzw_order shape:', xyzw_order.shape) + prob_order = reorder(prob, order) # [ batch, order, num_kp, resy, resx] + print('prob_order shape:', prob_order.shape) + + keys_uvd = labels['keys_uvd_L'] # [batch, num_kp, 4] + # [batch, order, num_kp, 4] + keys_uvd_order = tf.stack([keys_uvd] * num_order, axis=1) + + loss_kp = keypose_loss_kp(uvdw_order, keys_uvd_order, + mparams) # [batch, order] + loss_kp.set_shape([None, num_order]) + # [batch, order] + loss_proj = keypose_loss_proj(uvdw_pos_order, labels, mparams, num_order) + loss_proj.set_shape([None, num_order]) + loss_proj_adj = adjust_proj_factor(step, mparams.loss_proj_step) + + prob_label = labels['prob_label'] # [batch, num_kp, resy, resx] + # [batch, order, num_kp, resy, resx] + prob_label_order = tf.stack([prob_label] * num_order, axis=1) + loss_prob = keypose_loss_prob(prob_order, prob_label_order) + + loss_order = ( + mparams.loss_kp * loss_kp + + mparams.loss_proj * loss_proj_adj * loss_proj + + mparams.loss_prob * loss_prob) + + print('loss_order shape [batch, order]:', loss_order.shape) + loss = tf.reduce_min(loss_order, axis=1, keepdims=True) # shape [batch, 1] + print('loss shape [batch, 1]:', loss.shape) + + loss_mask = tf.cast(tf.equal(loss, loss_order), tf.float32) # [batch, order] + loss_mask3 = tf.expand_dims(loss_mask, -1) # [batch, order, 1] + loss_mask4 = tf.expand_dims(loss_mask3, -1) # [batch, order, 1, 1] + print('loss_mask shape [batch, order]:', loss_mask.shape) + + loss = tf.reduce_mean(loss) # Scalar, reduction over batch. + loss_kp = reduce_order(loss_kp, loss_mask, mean=True) + loss_proj = reduce_order(loss_proj, loss_mask, mean=True) + loss_prob = reduce_order(loss_prob, loss_mask, mean=True) + + uvdw = reduce_order(uvdw_order, loss_mask4) # [batch, num_kp, 4] + xyzw = reduce_order(xyzw_order, loss_mask4) # [batch, num_kp, 4] + print('xyzw shape:', xyzw.shape) + + if do_print: + tf.print( + ' ', + step, + 'Keypose loss:', + loss, + mparams.loss_kp * loss_kp, + mparams.loss_proj * loss_proj_adj * loss_proj, + mparams.loss_prob * loss_prob, + ' ', + loss_proj_adj, + uv_pix_raw[0, 0, :3], + uvdw_pos[0, 0, :3], + keys_uvd[0, 0, :3], + summarize=-1) + return loss, uvdw, xyzw + + +# +# Metrics and visualization. +# + + +def add_keypoints(img, uv, colors=None): + """Add keypoint markers to an image, using draw_bounding_boxes. + + Args: + img: [batch, vh, vw, 3] + uv: [batch, num_kp, 2], in normalized coords [-1,1], xy order. + colors: color palette for keypoints. + + Returns: + tf images with drawn keypoints. + """ + if colors is None: + colors = tf.constant([[0.0, 1.0, 0.0, 1.0]]) + else: + colors = tf.constant(colors) + uv = uv[:, :, :2] * 0.5 + 0.5 # [-1,1] -> [0,1] + keys_bb_ul = tf.stack([uv[:, :, 1], uv[:, :, 0]], axis=2) + keys_bb_lr = keys_bb_ul + 3.0 / tf.cast(tf.shape(img)[1], dtype=tf.float32) + keys_bb = tf.concat([keys_bb_ul, keys_bb_lr], axis=2) + print('Bounding box shape:', keys_bb.shape) + return tf.image.draw_bounding_boxes(img, tf.cast(keys_bb, dtype=tf.float32), + colors) + + +def add_keypoints_uv(img, uv, colors=None): + """Add keypoint markers to an image, using draw_bounding_boxes. + + Args: + img: [batch, vh, vw, 3] + uv: [batch, num_kp, 2], in image coords, xy order. + colors: color palette for drawing keypoints. + + Returns: + tf images with drawn keypoints. + """ + resy = img.shape[1] + resx = img.shape[2] + uvx = uv[:, :, 0] / resx + uvy = uv[:, :, 1] / resy + uv = tf.stack([uvx, uvy], axis=2) + return add_keypoints(img, (uv - 0.5) * 2.0, colors) + + +def uv_error(labels, uvdw, _): + diff = labels['keys_uvd_L'][..., :2] - uvdw[..., :2] + return tf.sqrt(tf.reduce_sum(tf.square(diff), axis=-1)) # [batch, num_kp] + + +def disp_error(labels, uvdw, _): + return tf.abs(labels['keys_uvd_L'][..., 2] - uvdw[..., 2]) # [batch, num_kp] + + +def world_error(labels, xyzw): + xyzw = tf.transpose(xyzw, [0, 2, 1]) # [batch, 4, num_kp] + # [batch, 4, num_kp] + gt_world_coords = project(labels['to_world_L'], labels['keys_uvd_L'], True) + sub = xyzw[:, :3, :] - gt_world_coords[:, :3, :] + wd = tf.square(sub) + wd = tf.reduce_sum(wd, axis=[-2]) # [batch, num_kp] result. + wd = tf.sqrt(wd) + return wd # [batch, num_kp] + + +def lt_2cm_error(labels, xyzw): + err = world_error(labels, xyzw) + lt = tf.less(err, 0.02) + return (100.0 * tf.cast(tf.math.count_nonzero(lt, axis=[-1]), tf.float32) / + tf.cast(tf.shape(err)[1], tf.float32)) # [batch] diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/nets.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/nets.py new file mode 100644 index 000000000..7c46a5ef1 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/nets.py @@ -0,0 +1,415 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""DNN definitions for KeyPose keypoint files. + +Adapted from 'Discovery of Latent 3D Keypoints via End-to-end +Geometric Reasoning' keypoint network. +""" + +import math + +import numpy as np +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras import layers + + +def dilated_cnn(inputs, + num_filters, + max_dilation, + dilation_rep, + filter_size, + dropout=0.0, + bn_decay=0.999, + bn_epsilon=1.0e-8, + bn_scale=False, + is_training=True): + """Constructs a base dilated convolutional network. + + Args: + inputs: Typically [batch, h, w, [3,6]] Input RGB images. Two 3-channel + images are concatenated for stereo. + num_filters: The number of filters for all layers. + max_dilation: Size of the last dilation of a series. Must be a power of + two.fine + dilation_rep: Number of times to repeat the last dilation. + filter_size: kernel size for CNNs. + dropout: >0 if dropout is to be applied. + bn_decay: batchnorm parameter. + bn_epsilon: batchnorm parameter. + bn_scale: True to scale batchnorm. + is_training: True if this function is called during training. + + Returns: + Output of this dilated CNN. + """ + # Progression of powers of 2: [1, 2, 4, 8 ... max_dliation]. + maxlog = int(math.log(max_dilation, 2)) + seq = [2**x for x in range(maxlog + 1)] + seq += [1] * (dilation_rep - 1) + net = inputs + for i, r in enumerate([1] + seq + seq + [1]): + # Split off head before the last dilation 1 convolutions. + if i == (len(seq) * 2 - dilation_rep + 2): + head = net + + fs = filter_size + net = layers.Conv2D( + num_filters, + fs, + dilation_rate=r, + padding='same', + kernel_regularizer=tf.keras.regularizers.l2(0.001), + use_bias=False, + trainable=is_training, + name='dil_conv_%d_%d' % (r, i))( + net) + # From https://arxiv.org/pdf/1905.05928.pdf; batchnorm then dropout + # slim layers has decay/momentum = 0.999, not 0.99. + # slim layers has scale=False, not scale=True. + net = layers.BatchNormalization( + scale=bn_scale, + epsilon=bn_epsilon, + trainable=is_training, + momentum=bn_decay)( + net, training=is_training) + net = layers.LeakyReLU(alpha=0.1)(net) + if dropout and i == len(seq): + net = layers.SpatialDropout2D(dropout)(net, training=is_training) + + return net, head + + +# Image normalization. These values are taken from ImageNet. +def norm_image(im): + mean = np.array([0.485, 0.456, 0.406]) + std = np.array([0.229, 0.224, 0.225]) + std_inv = 1.0 / std + return tf.multiply(tf.subtract(im, mean), std_inv) + + +# Integral calculation for centroid coordinates. +def meshgrid(w, h): + """Returns a meshgrid ranging from [-1,1] in x, y axes.""" + + rh = np.arange(0.5, h, 1) / (h / 2) - 1 + rw = np.arange(0.5, w, 1) / (w / 2) - 1 + ranx, rany = tf.meshgrid(rw, rh) + return tf.cast(ranx, tf.float32), tf.cast(rany, tf.float32) + + +# Keras layer for getting centroid coord by multiplying with a mesh field. +def k_reduce_mult_sum(tensor, mult, axis=None, name=None): + + def reduce_mult_sum(tensor, mult, axis): + return tf.reduce_sum(tensor * mult, axis=axis) + + if not axis: + axis = [-1] + return layers.Lambda( + reduce_mult_sum, arguments={ + 'mult': mult, + 'axis': axis + }, name=name)( + tensor) + + +# Keras layer for reducing sum. +def k_reduce_sum(tensor, axis=None, name=None): + + def reduce_sum(tensor, axis): + return tf.reduce_sum(tensor, axis=axis) + + if not axis: + axis = [-1] + return layers.Lambda(reduce_sum, arguments={'axis': axis}, name=name)(tensor) + + +def project(tmat, tvec, tvec_transpose=False): + """Projects homogeneous 3D XYZ coordinates to image uvd coordinates, or vv. + + Args: + tmat: has shape [[N,] batch_size, 4, 4] + tvec: has shape [[N,] batch_size, 4, num_kp] or [batch_size, num_kp, 4]. + tvec_transpose: True if tvec is to be transposed before use. + + Returns: + Has shape [[N,] batch_size, 4, num_kp] + """ + tp = tf.matmul(tmat, tvec, transpose_b=tvec_transpose) + # Using <3:4> instead of <3> preserves shape. + tp = tp / (tp[Ellipsis, 3:4, :] + 1.0e-10) + return tp + + +def project_hom(hom, tvec): + """Transforms homogeneous 2D uvw coordinates to 2D uv1 coordinates. + + Args: + hom: has shape [[N,] batch_size, 3, 3]. + tvec: has shape [[N,] batch_size, num_kp, 3]. + + Returns: + tensor with shape [[N,] batch_size, num_kp, 3]. + """ + tp = tf.matmul(hom, tvec, transpose_b=True) + # Using <2:3> instead of <2> preserves shape. + tp = tp / (tp[Ellipsis, 2:3, :] + 1.0e-10) + return tf.transpose(tp, [0, 2, 1]) + + +def to_pixel(vec, res): + """Converts a normalized image coord to pixel coords.""" + return (vec + 1.0) * res * 0.5 # [-1,1] + + +def to_norm_vec(vec, mparams, weight_disp=1.0): + """Converts a pixel-based tensor to normalized image coordinates.""" + # [..., num_kp, 3] + return tf.concat([ + vec[Ellipsis, 0:1] * 2.0 / mparams.modelx - 1.0, + vec[Ellipsis, 1:2] * 2.0 / mparams.modely - 1.0, + vec[Ellipsis, 2:3] / mparams.modelx * weight_disp + ], + axis=-1) + + +def to_norm(mat, mparams, weight_disp=1.0): + """Converts a pixel-based tensor to normalized image coordinates.""" + # [..., 4, num_kp] + return tf.concat([ + mat[Ellipsis, 0:1, :] * 2.0 / mparams.modelx - 1.0, + mat[Ellipsis, 1:2, :] * 2.0 / mparams.modely - 1.0, + mat[Ellipsis, 2:3, :] * 2.0 / mparams.modelx * weight_disp, mat[Ellipsis, 3:4, :] + ], + axis=-2) + + +def convert_uvd_raw(uvd, offsets, hom, mparams): + """Converts output of uvd integrals on cropped, warped img to original image. + + Args: + uvd: is [batch, num_kp, 3], with order u,v,d. + offsets: is [batch, 3]. + hom: is [batch, 3, 3]. + mparams: model parameters. + + Returns: + Various pixel and disparity results. + """ + num_kp = mparams.num_kp + uv_pix_raw = tf.stack([ + to_pixel(uvd[:, :, 0], mparams.modelx), + to_pixel(uvd[:, :, 1], mparams.modely), + tf.ones_like(uvd[:, :, 0]) + ], + axis=-1) # [batch, num_kp, 3] + uv_pix = project_hom(hom, uv_pix_raw) # [batch, num_kp, 3] + disp = uvd[:, :, 2] * mparams.modelx # Convert to pixels. + + # uvdw is in pixel coords of the original image. + uvdw = tf.stack([ + uv_pix[:, :, 0] + tf.stack([offsets[:, 0]] * num_kp, axis=1), + uv_pix[:, :, 1] + tf.stack([offsets[:, 1]] * num_kp, axis=1), + disp + tf.stack([offsets[:, 2]] * num_kp, axis=1), + tf.ones_like(uv_pix[:, :, 0]) + ], + axis=-1, + name='uvdw_out') + print('uvdw shape is [batch, num_kps, 4]:', uvdw.shape) + + # uvdw_pos is in pixel coords of original image, + # and has always-positive disparities. + uvdw_pos = tf.stack([ + uvdw[:, :, 0], uvdw[:, :, 1], + tf.nn.relu(uvdw[:, :, 2]) + 1.0e-5, + tf.ones_like(uvdw[:, :, 0]) + ], + axis=-1, + name='uvdw_pos') + + return uv_pix_raw, uv_pix, uvdw, uvdw_pos + + +def keypose_model(mparams, is_training): + """Constructs a Keras model that predicts 3D keypoints. + + Model input is left and optionally right image, modelx x modely x 3, float32. + Values are from 0.0 to 1.0 + + Args: + mparams: ConfigParams object use_stereo - True if right image is input. + num_filters - The number of filters for all layers. num_kp - The number of + keypoints. ... + is_training: True if training the model. + + Returns: + uv: [batch, num_kp, 2] 2D locations of keypoints. + d: [batch, num_kp] The inverse depth of keypoints. + prob_viz: A visualization of all predicted keypoints. + prob_vizs: A list of visualizations of each keypoint. + kp_viz: A visualization of GT keypoints. + img_out: The photometrically and geometrically altered image. + rot: rotation matrix modifying input: [batch, 3, 3]. + """ + print('Mparams in keypose_model:\n', mparams) + use_stereo = mparams.use_stereo + num_filters = mparams.num_filters + max_dilation = mparams.max_dilation + dilation_rep = mparams.dilation_rep + dropout = mparams.dropout + num_kp = mparams.num_kp + modelx = mparams.modelx + modely = mparams.modely + filter_size = mparams.filter_size + bn_decay = mparams.batchnorm[0] + bn_epsilon = mparams.batchnorm[1] + bn_scale = mparams.batchnorm[2] + + # Aux params input to the model. + offsets = keras.Input(shape=(3,), name='offsets', dtype='float32') + hom = keras.Input(shape=(3, 3), name='hom', dtype='float32') + to_world = keras.Input(shape=(4, 4), name='to_world_L', dtype='float32') + + # Images input to the model. + img_l = keras.Input(shape=(modely, modelx, 3), name='img_L', dtype='float32') + img_l_norm = layers.Lambda(norm_image)(img_l) + img_r = keras.Input(shape=(modely, modelx, 3), name='img_R', dtype='float32') + if use_stereo: + img_r_norm = layers.Lambda(norm_image)(img_r) + img_l_norm = layers.concatenate([img_l_norm, img_r_norm]) + + net, _ = dilated_cnn( + img_l_norm, + num_filters, + max_dilation, + dilation_rep, + filter_size, + bn_decay=bn_decay, + bn_epsilon=bn_epsilon, + bn_scale=bn_scale, + dropout=dropout, + is_training=is_training) + + print('Dilation net shape:', net.shape) + + # Regression to keypoint values. + if mparams.use_regress: + if dropout: + net = layers.SpatialDropout2D(dropout)(net, training=is_training) + + net = layers.Conv2D( + 64, + 1, + kernel_regularizer=tf.keras.regularizers.l2(0.001), + use_bias=False, + padding='valid')( + net) + net = layers.BatchNormalization( + scale=bn_scale, epsilon=bn_epsilon, momentum=bn_decay)( + net, training=is_training) + net = layers.LeakyReLU(alpha=0.1)(net) + net = layers.Conv2D( + 64, + 1, + kernel_regularizer=tf.keras.regularizers.l2(0.001), + use_bias=False, + padding='valid')( + net) + net = layers.BatchNormalization( + scale=bn_scale, epsilon=bn_epsilon, momentum=bn_decay)( + net, training=is_training) + net = layers.LeakyReLU(alpha=0.1)(net) + net = layers.Conv2D( + num_kp * 3, + 1, + kernel_regularizer=tf.keras.regularizers.l2(0.001), + padding='valid')(net) # [batch, h, w, num_kp * 3] + net = tf.reduce_mean(net, axis=[-3, -2]) # [batch, num_kp * 3] + print('Regress reduce mean shape:', net.shape) + + uvd = tf.reshape(net, [-1, num_kp, 3]) # [batch, num_kp, 3] + print('Regress uvd shape:', uvd.shape) + prob = tf.stack([tf.zeros_like(img_l[Ellipsis, 0])] * num_kp, axis=1) + disp_map = prob + # [batch, num_kp, h, w] + + else: + # The probability distribution map for keypoints. No activation. + prob = layers.Conv2D( + num_kp, + filter_size, + dilation_rate=1, + kernel_regularizer=tf.keras.regularizers.l2(0.001), + padding='same')( + net) + + # Disparity map. + disp_map = layers.Conv2D( + num_kp, + filter_size, + dilation_rate=1, + kernel_regularizer=tf.keras.regularizers.l2(0.001), + padding='same')( + net) + + # [batch_size, h, w, num_kp] + prob = layers.Permute((3, 1, 2))(prob) + disp_map = layers.Permute((3, 1, 2))(disp_map) + + # [batch_size, num_kp, h, w] + prob = layers.Reshape((num_kp, modely * modelx))(prob) + prob = layers.Softmax()(prob) + prob = layers.Reshape((num_kp, modely, modelx), name='prob')(prob) + disp = layers.multiply([prob, disp_map]) + disp = k_reduce_sum(disp, axis=[-1, -2], name='disp_out') + + ranx, rany = meshgrid(modelx, modely) + # Use centroid to find indices. + sx = k_reduce_mult_sum(prob, ranx, axis=[-1, -2]) + sy = k_reduce_mult_sum(prob, rany, axis=[-1, -2]) + # uv are in normalized coords [-1, 1], uv order. + uvd = layers.concatenate([sx, sy, disp]) + uvd = layers.Reshape((3, num_kp))(uvd) + uvd = layers.Permute((2, 1), name='uvd')(uvd) # [batch, num_kp, 3] + + uv_pix_raw, uv_pix, uvdw, uvdw_pos = convert_uvd_raw(uvd, offsets, hom, + mparams) + + xyzw = project(to_world, uvdw_pos, True) # [batch, 4, num_kp] + + model = keras.Model( + inputs={ + 'img_L': img_l, + 'img_R': img_r, + 'offsets': offsets, + 'hom': hom, + 'to_world_L': to_world + }, + outputs={ + 'uvd': uvd, + 'uvdw': uvdw, + 'uvdw_pos': uvdw_pos, + 'uv_pix': uv_pix, + 'uv_pix_raw': uv_pix_raw, + 'xyzw': xyzw, + 'prob': prob, + 'disp': disp_map + }, + name='keypose') + + model.summary() + return model diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/predict.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/predict.py new file mode 100644 index 000000000..d0924f29f --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/predict.py @@ -0,0 +1,214 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Pose estimation from keypoints using models. + +Given a 2D image or stereo pair, predict a set of 3D keypoints that +match the target examples. + +The should have a saved_model directory with + and +""" + +import glob +import os +import sys +import time +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import skimage as ski +import tensorflow as tf + +from keypose import utils + +try: + import cv2 # pylint: disable=g-import-not-at-top +except ImportError as e: + print(e) + + +def image_as_ubyte(image): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + return ski.img_as_ubyte(image) + + +def draw_circle(image, uvd, color, size=2): + # Filled color circle. + cv2.circle(image, (int(uvd[0]), int(uvd[1])), size, tuple(color), -1) + # White outline. + cv2.circle(image, (int(uvd[0]), int(uvd[1])), size + 1, (255, 255, 255)) + + +def predict(model_dir, image_dir, params, camera, camera_input, mesh_file=None): + """Predicts keypoints on all images in dset_dir, evaluates 3D accuracy.""" + + # Set up network for prediction. + mparams = params.model_params + num_kp = mparams.num_kp + + # Set up predictor from model directory. + model = tf.saved_model.load(model_dir) + predict_fn = model.signatures['serving_default'] + + colors = plt.cm.get_cmap('rainbow')(np.linspace(0, 1.0, num_kp))[:, :3] + colors = (colors * 255).tolist() + + # Set up mesh object, if the mesh file exists. + obj = None + if mesh_file and mesh_file != 'show': + obj = utils.read_mesh(mesh_file, num=300) + obj.large_points = False + + # Iterate over all images in image_dir. + total_time = 0.0 + count = 0 + mae_list = [] + + filenames = glob.glob(os.path.join(image_dir, '*_L.png')) + filenames.sort() + for fname in filenames: + # Read in and resize images if necessary. + print(fname) + im_l = utils.read_image(fname) + targs_pb = utils.read_target_pb(fname.replace('_L.png', '_L.pbtxt')) + kps_pb = targs_pb.kp_target + im_l = utils.resize_image(im_l, camera, camera_input, + targs_pb) # NB: changes targs_pb. + keys_uvd_l, to_world_l, visible_l = utils.get_keypoints(kps_pb) + + im_r = utils.read_image(fname.replace('_L.png', '_R.png')) + targs_pb = utils.read_target_pb(fname.replace('_L.png', '_R.pbtxt')) + kps_pb = targs_pb.kp_target + im_r = utils.resize_image(im_r, camera, camera_input, + targs_pb) # NB: changes kps_pb. + keys_uvd_r, _, _ = utils.get_keypoints(kps_pb) + + # Do cropping if called out in mparams. + if mparams.crop: + img0, img1, offs, visible = utils.do_occlude_crop( + im_l, + im_r, + keys_uvd_l, + keys_uvd_r, + mparams.crop, + visible_l, + dither=0.0, + var_offset=False) + if np.any(visible == 0.0): + print('Could not crop') + continue + offsets = offs.astype(np.float32) + hom = np.eye(3, dtype=np.float32) + to_world_l = to_world_l.astype(np.float32) + keys_uvd_l = keys_uvd_l.astype(np.float32) + + # Batch size of 1. + img_l = tf.constant(np.expand_dims(img0[:, :, :3], 0)) + img_r = tf.constant(np.expand_dims(img1[:, :, :3], 0)) + to_world_l = tf.constant(np.expand_dims(to_world_l, 0)) + keys_uvd_l = tf.constant(np.expand_dims(keys_uvd_l, 0)) + offsets = tf.constant(np.expand_dims(offsets, 0)) + hom = tf.constant(np.expand_dims(hom, 0)) + labels = {} + labels['keys_uvd_L'] = keys_uvd_l + labels['to_world_L'] = to_world_l + + # Now do the magic. + t0 = time.time() + preds = predict_fn( + img_L=img_l, + img_R=img_r, + to_world_L=to_world_l, + offsets=offsets, + hom=hom) + + if count > 0: # Ignore first time, startup is long. + total_time += time.time() - t0 + count += 1 + + xyzw = tf.transpose(preds['xyzw'], [0, 2, 1]) + + mae_3d = utils.world_error(labels, xyzw).numpy() + mae_list.append(mae_3d) + print('mae_3d:', mae_3d) + uvdw = preds['uvdw'][0, Ellipsis].numpy() + xyzw = xyzw[0, Ellipsis].numpy() + offsets = offsets[0, Ellipsis].numpy() + + # uv_pix_raw is in the coords of the cropped image used by the model. + uv_pix_raw = preds['uv_pix_raw'][0, Ellipsis].numpy() # [num_kp, 3] + + img_l = image_as_ubyte(img_l[0, Ellipsis].numpy()) + img_r = image_as_ubyte(img_r[0, Ellipsis].numpy()) + iml_orig = cv2.resize(img_l, None, fx=2, fy=2) + imr_orig = cv2.resize(img_r, None, fx=2, fy=2) + if obj: + p_matrix = utils.p_matrix_from_camera(camera) + q_matrix = utils.q_matrix_from_camera(camera) + xyzw_cam = utils.project_np(q_matrix, uvdw.T) + obj.project_to_uvd(xyzw_cam, p_matrix) + im_mesh = np.array(img_l) + im_mesh = obj.draw_points(im_mesh, offsets) + im_mesh = cv2.resize(im_mesh, None, fx=2, fy=2) + else: + im_mesh = np.zeros(iml_orig.shape, dtype=np.uint8) + + for i in range(uv_pix_raw.shape[0]): + draw_circle(img_l, uv_pix_raw[i, :2], colors[i]) + im_kps = cv2.resize(img_l, None, fx=2, fy=2) + im_large = cv2.vconcat( + [cv2.hconcat([iml_orig, imr_orig]), + cv2.hconcat([im_kps, im_mesh])]) + if mesh_file: + cv2.imshow('Left and right images; keypoints and mesh', im_large) + key = cv2.waitKey() + if key == ord('q'): + break + + print('Total time: %.1f for %d inferences, %.1f ms/inf' % + (total_time, count, total_time * 1000.0 / count)) + mae_list = np.concatenate(mae_list).flatten() + print('MAE 3D (m): %f' % np.mean(mae_list)) + + +def main(argv): + if not len(argv) >= 3: + print('Usage: ./predict.py [show|]') + exit(0) + model_dir = argv[1] + image_dir = argv[2] + mesh_file = None + if len(argv) > 3: + mesh_file = argv[3] + fname = os.path.join(model_dir, 'params.yaml') + with open(fname, 'r') as f: + params, camera_model, camera_image = utils.get_params( + param_file=f, + cam_file=os.path.join(model_dir, 'data_params.pbtxt'), + cam_image_file=os.path.join(image_dir, 'data_params.pbtxt')) + predict( + model_dir, + image_dir, + params, + camera_model, + camera_image, + mesh_file=mesh_file) + + +if __name__ == '__main__': + main(sys.argv) diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/requirements.txt b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/requirements.txt new file mode 100644 index 000000000..1121fba98 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/requirements.txt @@ -0,0 +1,6 @@ +numpy==1.19.5 +tensorflow==2.4.0 +opencv-python==4.6.0.66 +matplotlib==3.5.3 +scikit-image==0.19.3 +pyyaml==6.0 diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run.sh b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run.sh new file mode 100644 index 000000000..7927204d0 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run.sh @@ -0,0 +1,25 @@ +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +#!/bin/bash +set -e +set -x + +virtualenv -p python3 . +source ./bin/activate + +pip install tensorflow +pip install -r keypose/requirements.txt + +python -m keypose.run_test_load diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run_test_load.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run_test_load.py new file mode 100644 index 000000000..39d673f34 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/run_test_load.py @@ -0,0 +1,43 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Simple test to see if all the modules load.""" + +import matplotlib.pyplot as plt +import numpy as np +import skimage as ski +import tensorflow as tf + +from keypose import utils + +try: + import cv2 # pylint: disable=g-import-not-at-top +except ImportError as e: + print(e) + +print(cv2) +print(plt) +print(np) +print(ski) +print(tf) +print(utils) + + +def main(): + pass + + +if __name__ == '__main__': + main() diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/show_keypoints.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/show_keypoints.py new file mode 100644 index 000000000..6e1afb9bc --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/show_keypoints.py @@ -0,0 +1,113 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""From a directory of images and keypoint protobufs, shows images, with kps. + +Invocation: + $ python -m code/show_keypoints []. +""" + +import glob +import os +import sys + +import matplotlib.pyplot as plt +import numpy as np + +from keypose import utils + +try: + import cv2 # pylint: disable=g-import-not-at-top +except ImportError as e: + print(e) + +colors = 255 * plt.cm.get_cmap('rainbow')(np.linspace(0, 1.0, 10))[:, :3] + + +def draw_circle(image, uvd, color, size=3): + # Filled color circle. + cv2.circle(image, (int(uvd[0]), int(uvd[1])), size, color, -1) + # White outline. + cv2.circle(image, (int(uvd[0]), int(uvd[1])), size + 1, (255, 255, 255)) + + +def show_keypoints(image_dir, mesh_file): + """Display keypoints in a keypose data file.""" + print('Looking for images in %s' % image_dir) + filenames = glob.glob(os.path.join(image_dir, '*_L.png')) + if not filenames: + print("Couldn't find any PNG files in %s" % image_dir) + exit(-1) + filenames.sort() + print('Found %d files in %s' % (len(filenames), image_dir)) + + obj = None + if mesh_file: + obj = utils.read_mesh(mesh_file) + + for fname in filenames: + im_l = utils.read_image(fname) + im_r = utils.read_image(fname.replace('_L.png', '_R.png')) + im_mask = utils.read_image(fname.replace('_L.png', '_mask.png')) + im_border = utils.read_image(fname.replace('_L.png', '_border.png')) + cam, _, _, uvds, _, _, transform = utils.read_contents_pb( + fname.replace('_L.png', '_L.pbtxt')) + print(fname) + ret = show_kps(im_l, im_r, im_mask, im_border, + (cam, uvds, transform), obj) + if ret: + break + + +def show_kps(im_l, im_r, im_border, im_mask, kps, obj=None, size=3): + """Draw left/right images and keypoints using OpenCV.""" + cam, uvds, _ = kps + + im_l = cv2.cvtColor(im_l, cv2.COLOR_BGR2RGB) + im_r = cv2.cvtColor(im_r, cv2.COLOR_BGR2RGB) + + uvds = np.array(uvds) + for i, uvd in enumerate(uvds): + draw_circle(im_l, uvd, colors[i * 3], size) + + if obj: + p_matrix = utils.p_matrix_from_camera(cam) + q_matrix = utils.q_matrix_from_camera(cam) + xyzs = utils.project_np(q_matrix, uvds.T) + obj.project_to_uvd(xyzs, p_matrix) + im_l = obj.draw_points(im_l) + + cv2.imshow('Image Left', im_l) + cv2.imshow('Border', im_border) + cv2.imshow('Mask', im_mask) + + key = cv2.waitKey() + if key == ord('q'): + return True + return False + + +def main(): + image_dir = sys.argv[1] + if len(sys.argv) < 3: + mesh_file = None + else: + mesh_file = sys.argv[2] + + show_keypoints(image_dir, mesh_file) + + +if __name__ == '__main__': + main() diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/trainer.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/trainer.py new file mode 100644 index 000000000..29d95defc --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/trainer.py @@ -0,0 +1,201 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Keypose estimator training / eval functions. + +Adapted from 'Discovery of Latent 3D Keypoints via End-to-end +Geometric Reasoning' keypoint network. + +Given a 2D image and viewpoint, predict a set of 3D keypoints that +match the target examples. + +Can be instance or class specific, depending on training set. + +Typical invocation: +$ python3 -m keypose.trainer.py configs/bottle_0_t5 /tmp/model +""" +import npu_device as npu +npu.open().as_default() + +import os +import sys + +from tensorflow import estimator as tf_estimator +from tensorflow import keras + +from keypose import estimator as est +from keypose import inputs as inp +from keypose import utils + + +def train_and_eval(params, + model_fn, + input_fn, + keep_checkpoint_every_n_hours=0.5, + save_checkpoints_secs=100, + eval_steps=0, + eval_start_delay_secs=10, + eval_throttle_secs=100, + save_summary_steps=50): + """Trains and evaluates our model. + + Supports local and distributed training. + + Args: + params: ConfigParams class with model training and network parameters. + model_fn: A func with prototype model_fn(features, labels, mode, hparams). + input_fn: A input function for the tf.estimator.Estimator. + keep_checkpoint_every_n_hours: Number of hours between each checkpoint to be + saved. + save_checkpoints_secs: Save checkpoints every this many seconds. + eval_steps: Number of steps to evaluate model; 0 for one epoch. + eval_start_delay_secs: Start evaluating after waiting for this many seconds. + eval_throttle_secs: Do not re-evaluate unless the last evaluation was + started at least this many seconds ago + save_summary_steps: Save summaries every this many steps. + """ + + mparams = params.model_params + + run_config = tf_estimator.RunConfig( + keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours, + save_checkpoints_secs=save_checkpoints_secs, + save_summary_steps=save_summary_steps) + + if run_config.model_dir: + params.model_dir = run_config.model_dir + print('\nCreating estimator with model dir %s' % params.model_dir) + estimator = tf_estimator.Estimator( + model_fn=model_fn, + model_dir=params.model_dir, + config=run_config, + params=params) + + print('\nCreating train_spec') + train_spec = tf_estimator.TrainSpec( + input_fn=input_fn(params, split='train'), max_steps=params.steps) + + print('\nCreating eval_spec') + + def serving_input_receiver_fn(): + """Serving input_fn that builds features from placeholders. + + Returns: + A tf.estimator.export.ServingInputReceiver. + """ + modelx = mparams.modelx + modely = mparams.modely + offsets = keras.Input(shape=(3,), name='offsets', dtype='float32') + hom = keras.Input(shape=(3, 3), name='hom', dtype='float32') + to_world = keras.Input(shape=(4, 4), name='to_world_L', dtype='float32') + img_l = keras.Input( + shape=(modely, modelx, 3), name='img_L', dtype='float32') + img_r = keras.Input( + shape=(modely, modelx, 3), name='img_R', dtype='float32') + features = { + 'img_L': img_l, + 'img_R': img_r, + 'to_world_L': to_world, + 'offsets': offsets, + 'hom': hom + } + return tf_estimator.export.build_raw_serving_input_receiver_fn(features) + + class SaveModel(tf_estimator.SessionRunHook): + """Saves a model in SavedModel format.""" + + def __init__(self, estimator, output_dir): + self.output_dir = output_dir + self.estimator = estimator + self.save_num = 0 + + def begin(self): + ckpt = self.estimator.latest_checkpoint() + print('Latest checkpoint in hook:', ckpt) + ckpt_num_str = ckpt.split('.ckpt-')[1] + if (int(ckpt_num_str) - self.save_num) > 4000: + fname = os.path.join(self.output_dir, 'saved_model-' + ckpt_num_str) + print('**** Saving model in train hook: %s' % fname) + self.estimator.export_saved_model(fname, serving_input_receiver_fn()) + self.save_num = int(ckpt_num_str) + + saver_hook = SaveModel(estimator, params.model_dir) + + if eval_steps == 0: + eval_steps = None + eval_spec = tf_estimator.EvalSpec( + input_fn=input_fn(params, split='val'), + steps=eval_steps, + hooks=[saver_hook], + start_delay_secs=eval_start_delay_secs, + throttle_secs=eval_throttle_secs) + + if run_config.is_chief: + outdir = params.model_dir + if outdir is not None: + print('Writing params to %s' % outdir) + os.makedirs(outdir, exist_ok=True) + params.write_yaml(os.path.join(outdir, 'params.yaml')) + + print('\nRunning estimator') + tf_estimator.train_and_evaluate(estimator, train_spec, eval_spec) + + print('\nSaving last model') + ckpt = estimator.latest_checkpoint() + print('Last checkpoint:', ckpt) + ckpt_num_str = ckpt.split('.ckpt-')[1] + fname = os.path.join(params.model_dir, 'saved_model-' + ckpt_num_str) + print('**** Saving last model: %s' % fname) + estimator.export_saved_model(fname, serving_input_receiver_fn()) + + +def main(argv): + if not len(argv) >= 2: + print('Usage: ./trainer.py ' + '[model_dir (/tmp/model)]') + exit(0) + + config_file = argv[1] + if len(argv) > 2: + model_dir = argv[2] + else: + model_dir = '/tmp/model' + + fname = os.path.join(utils.KEYPOSE_PATH, config_file + '.yaml') + with open(fname, 'r') as f: + params, _, _ = utils.get_params(param_file=f) + dset_dir = os.path.join(utils.KEYPOSE_PATH, params.dset_dir) + # Configuration has the dset directory, now get more info from there. + with open(fname, 'r') as f: + params, _, _ = utils.get_params( + param_file=f, + cam_file=os.path.join(os.path.join(dset_dir, 'data_params.pbtxt'))) + params.model_dir = model_dir + params.dset_dir = dset_dir + + print('Parameters to train and eval:\n', params.make_dict()) + + train_and_eval( + params, + model_fn=est.est_model_fn, + input_fn=inp.create_input_fn, + save_checkpoints_secs=600, + eval_throttle_secs=600, + eval_steps=1000, + ) + + +if __name__ == '__main__': + main(sys.argv) diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/utils.py b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/utils.py new file mode 100644 index 000000000..ff7908d55 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/utils.py @@ -0,0 +1,1175 @@ +# coding=utf-8 +# Copyright 2022 The Google Research Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utilities for reading images, parsing protobufs, etc.""" + +import math +import os +import random + +from google.protobuf import text_format +import matplotlib as plt +import matplotlib.cm # pylint: disable=unused-import +import numpy as np +from skimage.draw import ellipse +from skimage.exposure import adjust_gamma +from skimage.filters import gaussian +from skimage.transform import ProjectiveTransform +from skimage.transform import resize +from skimage.transform import warp +import tensorflow as tf +import yaml + +from keypose import data_pb2 as pb + +try: + import cv2 # pylint: disable=g-import-not-at-top +except ImportError as e: + print(e) + +# Top level keypose directory. +KEYPOSE_PATH = os.path.join(os.getcwd(), 'keypose') + + +# Read image, including .exr images. +def read_image(fname): + ext = os.path.splitext(fname)[1] + if ext == '.exr': + print('reading exr file') + image = cv2.imread(fname, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH) + else: + image = cv2.imread(fname) + assert image is not None, 'Cannot open %s' % fname + return image + + +# Camera array to camera protobuf +def cam_array_to_pb(cam): + cam_pb = pb.Camera() + cam_pb.fx = cam[0] + cam_pb.fy = cam[1] + cam_pb.cx = cam[2] + cam_pb.cy = cam[3] + cam_pb.baseline = cam[4] + cam_pb.resx = cam[5] + cam_pb.resy = cam[6] + return cam_pb + + +# Camera protobuf to camera array +def cam_pb_to_array(cam_pb): + return [ + cam_pb.fx, cam_pb.fy, cam_pb.cx, cam_pb.cy, cam_pb.baseline, cam_pb.resx, + cam_pb.resy + ] + + +# Dummy transform that always produces 0 vector as result. +def dummy_to_uvd(): + to_uvd = np.zeros((4, 4)) + to_uvd[3, 3] = 1.0 + return to_uvd + + +# Zero uvd stack. +def dummy_keys_uvd(num): + uvd = np.zeros(4) + uvd[3] = 1.0 + return [uvd for _ in range(num)] + + +def k_matrix_from_camera(camera): + return np.array([ + [camera.fx, 0, camera.cx], # fx, cx + [0, camera.fy, camera.cy], # fy, cy + [0, 0, 1] + ]) + + +def q_matrix_from_camera(camera): + return np.array([ + [1.0, 0, 0, -camera.cx], # cx + [0, 1.0, 0, -camera.cy], # cy + [0, 0, 0, camera.fx], # fx + [0, 0, 1.0 / camera.baseline, 0] + ]) # baseline + + +def p_matrix_from_camera(camera): + return np.array([ + [camera.fx, 0, camera.cx, 0], # fx, cx + [0, camera.fy, camera.cy, 0], # fy, cy + [0, 0, 0, camera.fx * camera.baseline], # fx*baseline + [0, 0, 1.0, 0] + ]) + + +# Parse a transform protobuf. +def get_transform(trans_pb): + transform = np.array(trans_pb.element) + if transform.size == 16: + return transform.reshape((4, 4)) + return None + + +def get_keypoints(targ_pb): + """Parse a keypoint protobuf.""" + uvds = [] + visible = [] + focal_length = targ_pb.camera.fx + baseline = targ_pb.camera.baseline + for kp_pb in targ_pb.keypoints: + uvds.append( + np.array([kp_pb.u, kp_pb.v, focal_length * baseline / kp_pb.z, 1])) + visible.append(kp_pb.visible) + transform = get_transform(targ_pb.transform) + if transform is None: + world_t_uvd = None + else: + world_t_uvd = np.linalg.inv(transform).dot( + q_matrix_from_camera(targ_pb.camera)) + return np.array(uvds), world_t_uvd, np.array(visible) + + +# NOTE: xyzs are NOT world coords. xy are percent image coords, z is depth (m). +def get_contents_pb(targ_pb): + uvds, xyzs, visible = get_keypoints(targ_pb) + cam = targ_pb.camera + transform = get_transform(targ_pb.transform) + if transform is None: + return cam, None, None, uvds, xyzs, visible, transform + uvd_t_world = p_matrix_from_camera(cam).dot(transform) + world_t_uvd = np.linalg.inv(transform).dot(q_matrix_from_camera(cam)) + return cam, uvd_t_world, world_t_uvd, uvds, xyzs, visible, transform + + +# Reading text-formatted protobuf files +def read_from_text_file(path, proto): + with open(path, 'r') as file: + text_format.Parse(file.read(), proto) + return proto + + +def read_target_pb(path): + target_pb = pb.KeyTargets() + read_from_text_file(path, target_pb) + return target_pb + + +def read_keys_pb(path): + target_pb = pb.KeyTargets() + read_from_text_file(path, target_pb) + return target_pb.kp_target + + +# Returns a dict of the filename strings in a TfSet. +def read_tfset(path): + data_pb = pb.TfSet() + read_from_text_file(path, data_pb) + return data_pb + + +def make_tfset(train_names, val_names, name): + tfset_pb = pb.TfSet() + tfset_pb.train[:] = train_names + tfset_pb.val[:] = val_names + tfset_pb.name = name + return tfset_pb + + +# Read the contents of a target protobuf. +def read_contents_pb(path): + return get_contents_pb(read_target_pb(path).kp_target) + + +# Make a DataParams protobuf. +def make_data_params(resx, resy, num_kp, cam): + data_pb = pb.DataParams() + data_pb.camera.CopyFrom(cam) + data_pb.resx = resx + data_pb.resy = resy + data_pb.num_kp = num_kp + return data_pb + + +# Read contents of a camera protobuf. +def read_data_params(path): + data_pb = pb.DataParams() + read_from_text_file(path, data_pb) + cam = data_pb.camera + return data_pb.resx, data_pb.resy, data_pb.num_kp, cam + + +def write_to_text_file(path, proto): + with open(path, 'w') as file: + file.write(str(proto)) + + +def project_np(mat, vec): + """Projects homogeneous 3D XYZ coordinates to image uvd coordinates.""" + # has shape [4, N]. + # has shape [4, 4] + # Return has shape [4, N]. + + p = mat.dot(vec) + # Using <3:4> instead of <3> preserves shape. + p = p / (p[3:4, :] + 1.0e-10) + return p + + +# Maximum number of projection frames for losses. +MAX_TARGET_FRAMES = 5 + +# Standard image input parameters for the model. +DEFAULT_PARAMS = """ +batch_size: 32 +dset_dir: '' +model_dir: '' +steps: 80000 + +model_params: +model_params: + use_regress: True # Use regression or integration. + batchnorm: [0.999, 1.0e-8, False] + num_filters: 48 # Number of filters across DCNN. + filter_size: 3 + max_dilation: 32 + dilation_rep: 1 + dropout: 0.0 + disp_default: 30 + sym: [0] + input_sym: [0] # Mix up symmetric ground truth values. + use_stereo: true + visible: true # Use only samples with visible keypoints. + + crop: [180, 120, 30] # Crop patch size, WxH, plus disp offset. + dither: 20.0 # Amount to dither the crop, in pixels. + + loss_kp: 1.0 + loss_kp_step: [0, 200] + loss_prob: 0.001 + loss_proj: 2.5 + loss_proj_step: [10000, 20000] + loss_reg: 0.01 + loss_dispmap: 1.0 + loss_dispmap_step: [5000, 10000] + + noise: 4.0 + occ_fraction: 0.2 + kp_occ_radius: 0.0 # Radius for occlusion for real-world keypoints. + blur: [1.0, 4.0] # Min, max in pixels. + motion: [0, 0, 0, 0] # Motion blur, min/max in pixels, min/max in angle (deg). + gamma: [0.8, 1.2] + + rot: [0.0, 0.0, 0.0] # In degrees, [X-axis, Y-axis, Z-axis] + + # Homography parameters [X-axis, Y-axis] + shear: [] # Use X-axis only for stereo [10,0]. + scale: [] # [min, max] applied only on the Y axis. + flip: [] # Use Y-axis only for stereo [False,True]. +""" + + +def get_params(param_file=None, + cam_file=None, + cam_image_file=None, + defaults=DEFAULT_PARAMS): + """Returns default or overridden user-specified parameters, and cam params.""" + + # param_file points to a yaml string. + # cam_file points to a camera pbtxt. + # cam_image_file points to a camera pbtxt. + + params = ConfigParams(defaults) + if param_file: + params.merge_yaml(param_file) + mparams = params.model_params + + cam = None + if cam_file: + print('Model camera params file name is: %s' % cam_file) + resx, resy, num_kp, cam = read_data_params(cam_file) + + mparams.resx = resx + mparams.resy = resy + mparams.num_kp = num_kp + mparams.modelx = resx + mparams.modely = resy + mparams.disp_default /= mparams.resx # Convert to fraction of image. + + if mparams.crop: + mparams.modelx = int(mparams.crop[0]) + mparams.modely = int(mparams.crop[1]) + + cam_image = None + if cam_image_file: + print('Image camera params file name is: %s' % cam_image_file) + _, _, _, cam_image = read_data_params(cam_image_file) + + print('MParams:', mparams.make_dict()) + return params, cam, cam_image + + +# General configuration class for referencing parameters using dot notation. +class ConfigParams: + """General configuration class for referencing params using dot notation.""" + + def __init__(self, init=None): + if init: + self.merge_yaml(init) + + def make_dict(self): + ret = {} + for k in self.__dict__: + val = self.__dict__[k] + if isinstance(val, self.__class__): + ret[k] = val.make_dict() + else: + ret[k] = val + return ret + + # No nesting. + def make_shallow_dict(self): + ret = {} + for k in self.__dict__: + val = self.__dict__[k] + if not isinstance(val, self.__class__): + ret[k] = val + return ret + + def read_yaml(self, fname): + with open(fname, 'r') as f: + ret = self.merge_yaml(f) + return ret + + def write_yaml(self, fname): + with open(fname, 'w') as f: + yaml.dump(self.make_dict(), f, default_flow_style=None) + + def merge_dict(self, p_dict): + """Merges a dictionary into this params.""" + for k in p_dict: + val = p_dict[k] + if k == 'default_file': + print('Default file:', val) + fname = os.path.join(KEYPOSE_PATH, val + '.yaml') + print('Default file fname:', fname) + self.read_yaml(fname) + continue + if isinstance(val, dict): + if getattr(self, k, None): + sub_params = getattr(self, k) + sub_params.merge_dict(val) + else: + sub_params = self.__class__() + sub_params.merge_dict(val) + setattr(self, k, sub_params) + else: + setattr(self, k, val) + + def merge_yaml(self, yaml_str): + try: + ret = yaml.safe_load(yaml_str) + except yaml.YAMLError as exc: + print('Error in loading yaml string') + print(exc) + return False + self.merge_dict(ret) + return True + + # Takes a string of the form 'a:1,b:2,c:[1,2],d:abcd', etc. + # No nesting. + def merge_str(self, p_str): + items = p_str.split('=') + assert len(items) >= 2 + # pylint: disable=g-complex-comprehension + items = items[:1] + [ + item for v in items[1:-1] for item in v.rsplit(',', 1) + ] + items[-1:] + y_list = [items[i] + ': ' + items[i + 1] for i in range(0, len(items), 2)] + y_str = '\n'.join(y_list) + self.merge_yaml(y_str) + + # Returns a string suitable for reading back in. Does not allow + # nested ConfigParams. + def __repr__(self): + # Checks float repr for not having a decimal point in scientific notation. + # Checks for None and returns empty string. + def yaml_str(obj): + if isinstance(obj, type(None)): + return '' + if isinstance(obj, list): + return '[' + ','.join([yaml_str(x) for x in obj]) + ']' + ret = str(obj) + if isinstance(obj, float): + if 'e' in ret and '.' not in ret: + return '.0e'.join(ret.split('e')) + return ret + + ivars = self.make_shallow_dict() + s = ','.join([k + '=' + yaml_str(ivars[k]) for k in ivars]) + return s.replace(' ', '') + + +# uint8 image [0,255] to float [0,1] +def image_uint8_to_float(image): + if image.dtype == np.float32: + return image + image = image.astype(np.float32) * (1.0 / 255.0) + image_np = np.clip(image, 0.0, 1.0) + return image_np + + +def resize_image(image, cam, cam_image, targs_pb): + """Resize an image using scaling and cropping; changes kps_pb to correspond.""" + resx, resy = int(cam_image.resx), int(cam_image.resy) + nxs, nys = int(cam.resx), int(cam.resy) + fx = cam_image.fx + nfx = cam.fx + scale = fx / nfx + + crop = resy - nys * scale, resx - nxs * scale + cropx, cropy = 0, 0 + if crop[0] > 1.5: + cropy = int(round(crop[0] * 0.5)) + if crop[1] > 1.5: + cropx = int(round(crop[1] * 0.5)) + + # Resize image. + image = image[cropy:resy - cropy, cropx:resx - cropx, :] + image = resize(image, (nys, nxs), mode='constant') # Converts to float. + image = image.astype(np.float32) + + def scale_cam(cam_pb): + cam_pb.fx /= scale + cam_pb.fy /= scale + cam_pb.cx = (cam_pb.cx - cropx) / scale + cam_pb.cy = (cam_pb.cy - cropy) / scale + cam_pb.resx = nxs + cam_pb.resy = nys + + def resize_target(targ_pb): + scale_cam(targ_pb.camera) + for kp in targ_pb.keypoints: + kp.u = (kp.u - cropx) / scale + kp.v = (kp.v - cropy) / scale + kp.x = kp.u / nxs + kp.y = kp.v / nys + if kp.u < 0 or kp.u >= nxs or kp.v < 0 or kp.v >= nys: + kp.visible = 0.0 + + resize_target(targs_pb.kp_target) + for targ_pb in targs_pb.proj_targets: + resize_target(targ_pb) + + return image + + +def rotation_transform(x_rot, y_rot, z_rot): + """Creates a rotation transform with rotations around the three axes. + + Args: + x_rot: rotate around X axis (degrees). + y_rot: rotate around Y axis (degrees). + z_rot: rotate around Z axis (degrees). + + Returns: + 4x4 transform. + """ + x_rot = np.pi * x_rot / 180.0 + xt = np.array([[1.0, 0, 0, 0], [0, np.cos(x_rot), -np.sin(x_rot), 0.0], + [0, np.sin(x_rot), np.cos(x_rot), 0.0], [0, 0, 0, 1]]) + y_rot = np.pi * y_rot / 180.0 + yt = np.array([[np.cos(y_rot), 0, np.sin(y_rot), 0], [0, 1, 0, 0], + [-np.sin(y_rot), 0, np.cos(y_rot), 0], [0, 0, 0, 1]]) + z_rot = np.pi * z_rot / 180.0 + zt = np.array([[np.cos(z_rot), -np.sin(z_rot), 0, 0], + [np.sin(z_rot), np.cos(z_rot), 0, 0], [0, 0, 1, 0], + [0, 0, 0, 1]]) + return xt.dot(yt).dot(zt) + + +# rotation of the camera +def rotate_camera(rotation, image, camera, transform, key_pts): + """Rotates a camera around its optical axis. + + Args: + rotation: 4x4 rotation transform, c'_R_c + image: Camera image, h x w x 4 (or 3). + camera: 7-element camera parameters (fx, fy, cx, cy, baseline, resx, resy) + transform: 4x4 transform matrix, w_T_c (camera-to-world). + key_pts: Nx4 u,v,d,w image keypoints, in pixel coordinates. + + Returns: + Rotated image, zero-filled + Updated transform w_T_c' = w_T_c * P * c_R_c' * Q + updated keypoints u,v,d,w, Nx4 + visibility vector for keypoint, N + + Keypoints are converted by P * c'_R_c * Q + """ + + def in_bounds(pt, bounds): + if (pt[0] >= 0 and pt[0] <= bounds[0] and pt[1] >= 0 and + pt[1] <= bounds[1]): + return 1.0 + else: + return 0.0 + + cam_pb = cam_array_to_pb(camera) + pmat = p_matrix_from_camera(cam_pb) + qmat = q_matrix_from_camera(cam_pb) + tp = np.dot(transform, np.dot(pmat, np.dot(np.linalg.inv(rotation), qmat))) + key_pts_p = project_np( + np.dot(pmat, np.dot(rotation, qmat)), np.transpose(key_pts)) + kmat = k_matrix_from_camera(cam_pb) + hmat = kmat.dot(rotation[:3, :3].transpose()).dot(np.linalg.inv(kmat)) + image_np = np.clip(image, 0.0, 0.9999) + warped = warp(image_np, ProjectiveTransform(matrix=hmat)) + visible = np.array([ + in_bounds(key_pts_p[:2, i], camera[5:7]) for i in range(key_pts.shape[0]) + ]) + return warped, tp, key_pts_p.transpose(), visible + + +def warp_homography(res, scale, shear, flip): + """Returns a homography for image scaling, shear and flip. + + Args: + res: resolution of the image, [x_res, y_res]. + scale: scale factor [x_scale, y_scale]. + shear: shear in [x_deg, y_deg]. + flip: boolean [x_flip, y_flip]. + """ + center_mat = np.eye(3) + center_mat[0, 2] = -res[0] / 2.0 + center_mat[1, 2] = -res[1] / 2.0 + cmat_inv = np.linalg.inv(center_mat) + + flip_mat = np.eye(3) + if flip[0]: + flip_mat[0, 0] = -1 + if flip[1]: + flip_mat[1, 1] = -1 + + shear_mat = np.eye(3) + shear_mat[0, 1] = math.tan(math.radians(shear[0])) + shear_mat[1, 0] = math.tan(math.radians(shear[1])) + + scale_mat = np.eye(3) + scale_mat[0, 0] = scale[0] + scale_mat[1, 1] = scale[1] + + return cmat_inv.dot(scale_mat.dot(shear_mat.dot(flip_mat.dot(center_mat)))) + + +def do_rotation(image, image2, transform, camera, key_pts, visible, rotation): + """Add a random rotation about the camera centerpoint. + + Args: + image: HxWx4 image, left image if stereo; can be uint8 or float. + image2: HxWx4 image, right image if stereo, or None + transform: 4x4 to_world transform. + camera: 7-element camera parameters. + key_pts: Nx4 uvdw keypoints. + visible: Visibility prediate for keypoints. + rotation: Rotation as 3-tuple, XYZ axes. + + Returns: + image: Warped by random rotation, float32. + transform: Updated to_world transform. + key_pts: updated uvdw keypoints. + visible: visibility vector for the keypoints. + """ + image = image_uint8_to_float(image) + image2 = image_uint8_to_float(image2) + area, _ = get_area(image) + if area < 10: # Something is wrong here. + return image, image2, transform, key_pts, visible + + rotation = (float(rotation[0]), float(rotation[1]), float(rotation[2])) + while True: + rot = rotation_transform( + random.uniform(-rotation[0], rotation[0]), + random.uniform(-rotation[1], rotation[1]), + random.uniform(-rotation[2], rotation[2])) + image_p, transform_p, key_pts_p, visible_p = rotate_camera( + rot, image, camera, transform, key_pts) + area_p, _ = get_area(image) + if float(area_p) / area > 0.6: + if image2 is not None: + image2_p, _, _, _ = rotate_camera(rot, image2, camera, transform, + key_pts) + else: + image2_p = image_p + break + + # Warp function converts images to float64, this converts back. + return (image_p.astype(np.float32), image2_p.astype(np.float32), + transform_p.astype(np.float32), key_pts_p.astype(np.float32), + visible_p.astype(np.float32)) + + +def do_2d_homography(image, image2, scale, shear, flip, mirrored, split): + """Add random 2D transforms to input images. + + Images are warped according to the 2D transforms of scaling, + shear and flip. The 2D homogenous transform inverse is returned, + so that keypoints can be adjusted after they are predicted. + Transforms that preserve horizontal epipolar lines are vertical flip, + X-axis shear, mirroring, and scaling. + TODO: visibility analysis. + + Args: + image: HxWx4 image, left image if stereo; can be uint8 or float. + image2: HxWx4 image, right image if stereo, or None + scale: floating point bounds, uniform random scale, e.g., [0.8, 1.2]. + shear: x,y shear max bounds for uniform random shear, in degrees. + flip: [True, True] if images are randomly flipped horizontal, vertical. + mirrored: True if images are mirrored. + split: train / eval split. + + Returns: + image: Warped by random transform, float32. + image2: Warped by same random transform, float32. + homography: 3x3 homography matrix for the warp. + """ + if (not mirrored) and not (split == 'train' and (scale or shear or flip)): + return image, image2, np.eye(3, dtype=np.float32) + + if not scale: + scale = [1.0, 1.0] + if not shear: + shear = [0.0, 0.0] + if not flip: + flip = [False, False] + image = image_uint8_to_float(image) + image2 = image_uint8_to_float(image2) + if mirrored: + flip = [False, True] + else: + flip = [random.choice([False, flip[0]]), random.choice([False, flip[1]])] + hom = warp_homography((image.shape[1], image.shape[0]), [ + 1.0, random.uniform(scale[0], scale[1]) + ], [random.uniform(-shear[0], shear[0]), + random.uniform(-shear[1], shear[1])], flip) + if np.allclose(hom, np.eye(3)): + return image, image2, np.eye(3, dtype=np.float32) + hom_inv = np.linalg.inv(hom) + image_p = warp_2d(image, hom_inv) + image2_p = warp_2d(image2, hom_inv) + # Warp function converts images to float64, this converts back. + return (image_p.astype(np.float32), image2_p.astype(np.float32), + hom_inv.astype(np.float32)) + + +def warp_2d(image, hom): + image_np = np.clip(image, 0.0, 0.9999) + warped = warp(image_np, ProjectiveTransform(matrix=hom)) + return warped + + +# Returns a 2D gaussian centered on mean (pix coords, float) with +# variance var (pixels, float). +def gauss2d(mean, var, size): + x = np.arange(0, size[0]) + y = np.arange(0, size[1]) + x, y = np.meshgrid(x, y) + mx, my = mean + vx, vy = var + return np.float32(1. / (2. * np.pi * vx * vy) * + np.exp(-((x - mx)**2. / (2. * vx**2.) + (y - my)**2. / + (2. * vy**2.)))) + + +# Normalize so the peak is 1.0. +def norm_gauss2d(mean, var, size): + g2d = gauss2d(mean, var, size) + m = np.max(g2d) + if m <= 0.0: + return g2d + return g2d * (1.0 / np.max(g2d)) + + +# Make an inverse gaussian for exclusion of a prob field. +def inv_gauss(mean, var, size): + g = gauss2d(mean, var, size) + m = np.max(g) + g_inv = (m - g) * (1.0 / m) + return g_inv + + +def project_uvd(mat, uvd, offset): + uvw = np.array([uvd[0] - offset[0], uvd[1] - offset[1], 1.0]) + uvwt = mat.dot(uvw) + return uvwt[:2] / (uvwt[2] + 1e-10) + + +def do_vertical_flip(image, image2, mirrored): + """Flip image vertically. + + The 2D homogenous transform inverse is returned, + so that keypoints can be adjusted after they are predicted. + + Args: + image: HxWx4 image, left image if stereo; can be uint8 or float. + image2: HxWx4 image, right image if stereo, or None. + mirrored: True if image is mirrored. + + Returns: + image: flipped vertically. + image2: flipped vertically. + homography: 3x3 homography matrix for vertical flip. + """ + if not mirrored: + return image, image2, np.eye(3, dtype=np.float32) + image = image_uint8_to_float(image) + image2 = image_uint8_to_float(image2) + image_p = np.flipud(image) + image2_p = np.flipud(image2) + hom = warp_homography( + (image.shape[1], image.shape[0]), + [1.0, 1.0], # Scale (none). + [0.0, 0.0], # Shear (none). + [False, True]) + return image_p, image2_p, np.linalg.inv(hom).astype(np.float32) + + +# Returns gaussian 2Ds with shape [num_kpts, h, w]. +# keys_uvd has shape [num_kps, 4] +def do_spatial_prob(keys_uvd, hom, offset, var, size): + hom_inv = np.linalg.inv(hom) + uvs = [project_uvd(hom_inv, uvd, offset) for uvd in keys_uvd] + probs = [inv_gauss(uv, [var, var], size) for uv in uvs] + return np.array(probs, dtype=np.float32) + + +# Largest fraction of object that can be occluded. +def do_occlude(image, image2, occ_fraction=0.0): + """Add an elliptical occlusion to the RGBA image. + + Args: + image: RGBA images with A channel @ 255 for valid object. + image2: RGBA images, right stereo. + occ_fraction: fraction of image to be occluded. + + Returns: + Modified image. + Modifies image in place. + """ + area, inds = get_area(image) + if area < 50: + return image, image2 + radius = 2.0 * np.sqrt(area / np.pi) * occ_fraction + + for _ in range(0, 1): + i = random.randint(0, area - 1) + ind = (inds[0][i], inds[1][i]) + rr, cc = ellipse( + ind[0], + ind[1], + random.uniform(1.0, radius), + random.uniform(1.0, radius), + shape=image.shape[0:2], + rotation=random.uniform(-1.0, 1.0) * np.pi) + image[rr, cc, 3] = 0 + image2[rr, cc, 3] = 0 + + return image, image2 + + +def do_motion_blur(image, distance, angle): + """Random fake motion blur by compositing in the x,y plane of the image. + + Args: + image: tensor of images, floating point [0,1], 3 or 4 channels. + distance: how far to move, in pixels . + angle: how much to rotate, in degrees . + + Returns: + Updated image with motion blur. + """ + if not distance[1]: # No blur. + return image + dist = random.uniform(*distance) * 0.5 + ang = math.radians(random.uniform(*angle)) + x, y = dist * math.cos(ang), dist * math.sin(ang) + rows, cols = image.shape[:2] + im = np.array([[1, 0, x], [0, 1, y]]) + im1 = cv2.warpAffine(image, im, (cols, rows)) + im = np.array([[1, 0, 2 * x], [0, 1, 2 * y]]) + im2 = cv2.warpAffine(image, im, (cols, rows)) + im = np.array([[1, 0, -x], [0, 1, -y]]) + im3 = cv2.warpAffine(image, im, (cols, rows)) + im = np.array([[1, 0, -2 * x], [0, 1, -2 * y]]) + im4 = cv2.warpAffine(image, im, (cols, rows)) + im = (image + im1 + im2 + im3 + im4) * 0.2 + np.clip(im, 0.0, 1.0, im) + return im + + +def do_composite(image, bg_fname, sigma, motion, noise, gamma): + """Composite a background image onto the foreground. + + Args: + image: original image, floating point [0,1]. + bg_fname: background image file name, None or empty string if none. + sigma: blur in pixels; single value or range. + motion: 4-tuple . + noise: pixel noise in the range 0-255, either single value or range. + gamma: gamma correction to be applied to image, 0 for none. + + Returns: + Updated image. + """ + + def make_random(x): + # Arg x can be list, tuple, numpy.ndarray + if isinstance(x, list) or isinstance(x, tuple): + x = np.array(x) + assert isinstance( + x, np.ndarray), 'Argument to do_composite must be list or array' + if x.size == 0: + return None + elif x.size == 1: + return x[0] + else: + return random.uniform(x[0], x[1]) + + if motion[1]: + image = do_motion_blur(image, motion[:2], motion[2:]) + + ys, xs, _ = image.shape + if bg_fname: + scene = read_image(bg_fname) * (1.0 / 255.0) + if random.choice([True, False]): + scene = np.flipud(scene) + if random.choice([True, False]): + scene = np.fliplr(scene) + yss, xss = scene.shape[0], scene.shape[1] + assert yss > ys, 'Background image must be larger than training image' + assert xss > xs, 'Background image must be larger than training image' + + # Adjust gamma of image. + gamma = make_random(gamma) + if gamma is not None: + image[:, :, :3] = adjust_gamma(image[:, :, :3], gamma) + # Add noise to object. + noise = make_random(noise) + if noise is not None: + image[:, :, :3] += np.random.randn(ys, xs, 3) * noise * 0.5 / 255. + np.clip(image, 0.0, 1.0, image) + + # Cut out ellipse where image alpha is 0. + if bg_fname: + ul_y = random.randint(0, yss - ys - 1) + ul_x = random.randint(0, xss - xs - 1) + scene_crop = scene[ul_y:ul_y + ys, ul_x:ul_x + xs, :] + mask = image[:, :, 3] + rgbmask = np.stack([mask, mask, mask], axis=2) + image[:, :, :3] = scene_crop * (1.0 - rgbmask) + image[:, :, :3] * rgbmask + else: + image[image[:, :, 3] == 0, 0:3] = 0.5 + + # Add gaussian blur and noise. + sigma = make_random(sigma) + im = np.copy(image[:, :, :3]) # Need copy to preserve float32 + gaussian(image[:, :, :3], sigma, multichannel=True, output=im) + # Add noise to whole scene, after blur. + if noise is not None: + im[:, :, :3] += np.random.randn(ys, xs, 3) * noise * 0.5 / 255. + np.clip(im, 0.0, 1.0, im) + + return im + + +# Returns area in pixels. +# Works for both uint8 and float32 images. +def get_area(image): + inds = np.nonzero(image[:, :, 3] > 0) + area = inds[0].shape[0] + return area, inds + + +def do_occlude_crop(image, + image2, + key_pts, + key_pts_r, + crop, + visible, + dither, + var_offset=False): + """Crop area around the object. + + Crop is [W, H, R]', where 'R' is right-disparity offset; or else []. + Images can be either floating-point or uint8. + + Args: + image: left image. + image2: right image. + key_pts: left keypoints. + key_pts_r: right keypoints. + crop: crop is [W, H, R]', where 'R' is right-disparity offset; or else []. + visible: visibility status of keypoints, modified by function. + dither: amount to dither the crop. + var_offset: vary the offset between left and right images. + + Returns: + image: cropped left image. + image2: cropped right image. + offset: offset of the crop in the original image. + visible: visibility status of the keypoints. + """ + + offset = np.array([0, 0, 0], dtype=np.float32) + crop = np.array(crop) + if crop.size == 0: + return image, image2, offset, visible + nxs, nys = crop[0], crop[1] + + def do_crop(im, left_x, top_y, margin=10.0): + y, x, _ = im.shape + x -= margin + y -= margin + right_x = left_x + nxs + bot_y = top_y + nys + if (left_x < margin or left_x > x or right_x < margin or right_x > x or + top_y < margin or top_y > y or bot_y < margin or bot_y > y): + visible[:] = 0.0 + return im[0:nys, 0:nxs, :] + return im[top_y:bot_y, left_x:right_x, :] + + centroid = np.mean(key_pts, axis=0)[0:2] + centroid += np.random.uniform(low=-dither, high=dither, size=(2)) + off_x = int(centroid[0] - nxs / 2 - (nxs - nys) / 2) + off_y = int(centroid[1] - nys / 2) + image = do_crop(image, off_x, off_y) + off_d = crop[2] + if var_offset: + off_d = int(centroid[0] - np.mean(key_pts_r, axis=0)[0]) + image2 = do_crop(image2, off_x - off_d, off_y) + offset = np.array([off_x, off_y, off_d], dtype=np.float32) + return image, image2, offset, visible + + +# Meshing functions. +def farthest_point_sampling(point_set, k): + """Find the k most spread-out poses of a set of poses; translation only.""" + num_pts = point_set.shape[0] + start_idx = np.random.randint(num_pts) + existing_set = np.expand_dims(point_set[start_idx], axis=0) + rest_set = np.copy(point_set) + np.delete(rest_set, start_idx, 0) + + existing_indices = [start_idx] + rest_indices = np.arange(num_pts) + np.delete(rest_indices, start_idx) + + for _ in range(k - 1): + dist = ( + np.sum(np.square(existing_set), axis=1, keepdims=True) + + np.sum(np.square(rest_set.T), axis=0, keepdims=True) - + np.dot(existing_set, rest_set.T) * 2) + min_dist = dist.min(axis=0) + max_idx = min_dist.argmax() + existing_set = np.concatenate( + [existing_set, np.expand_dims(rest_set[max_idx], axis=0)], axis=0) + existing_indices.append(rest_indices[max_idx]) + np.delete(rest_set, max_idx, 0) + np.delete(rest_indices, max_idx) + + return existing_set, existing_indices + + +# Class for holding a CAD object and its keypoints. +class MeshObj: + """Class for holding a CAD object and its keypoints.""" + + def __init__(self): + self.keypoints = np.array([]) + self.vertices = np.array([]) + self.large_points = True + + def read_obj(self, path, num=300): + """Read in a .obj file, parse into vertices and keypoints.""" + # Vertices are in label "o mesh". + # Keypoints are in label "o kp.NNN". + # Just read vertices, not other elements of the .obj file. + kps = {} + with open(path, 'r') as f: + line = f.readline() + while True: + if not line: + break + if len(line) > 2 and line[:2] == 'o ': + # New mesh. + name = line[2:-1] + vertices, line = self._read_sub_obj(f) + if 'mesh' in name: + self.vertices = vertices + if 'kp' in name: # Keypoint object. + kp_num = int(name.split('.')[1]) + kp_val = np.mean(vertices, axis=0) + kps[kp_num] = kp_val + else: + line = f.readline() + keypoints = [kps[i] for i in range(len(kps))] + if not keypoints: + print('Did not find any keypoints') + return + self.keypoints = np.array(keypoints) + self.keypoints = np.concatenate( + [self.keypoints, np.ones((self.keypoints.shape[0], 1))], axis=-1) + self.xyzw = np.concatenate( + [self.vertices, np.ones((self.vertices.shape[0], 1))], axis=-1) + self.make_reduced(num) + + def make_reduced(self, num): + self._make_colored_subset(num) + self.xyzw_reduced = np.concatenate( + [self.vertices_reduced, + np.ones((self.vertices_reduced.shape[0], 1))], + axis=-1) + + def _read_sub_obj(self, f): + """Read in all the vertices.""" + # First read to the beginning of vertices. + line = '' + vertices = [] + while True: + line = f.readline() + if not line: + return None + if 'v ' in line: + break + + # Now process all vertices. + while True: + elems = line[:-1].split(' ') + if elems[0] != 'v': + break + vertices.append(np.array([float(x) for x in elems[1:]])) + line = f.readline() + if not line: + break + return np.array(vertices), line + + # Pick a subset of colored points for display. + def _make_colored_subset(self, num): + self.vertices_reduced, _ = farthest_point_sampling(self.vertices, num) + colors = plt.cm.get_cmap('spring') + zs = self.vertices_reduced[:, 2] + self.reduced_colors = (255 * + colors(np.interp(zs, (zs.min(), zs.max()), + (0, 1)))[:, :3]).astype(np.uint8) + + def project_to_uvd(self, xyzs, p_matrix): + """Does a transform from CAD coords to kps coords, then projects to uvd.""" + kps_t_mesh = ortho_procrustes(self.keypoints, xyzs.T[:, :3]) + uvd_t_mesh = p_matrix.dot(kps_t_mesh) + self.uvds = project_np(uvd_t_mesh, self.xyzw.T).T + self.uvds_reduced = project_np(uvd_t_mesh, self.xyzw_reduced.T).T + + def draw_points(self, image, offsets=(0, 0)): + """Draws u,v points on an image as circles.""" + for i, pt in enumerate(self.uvds_reduced): + u = int(pt[0] - offsets[0]) + v = int(pt[1] - offsets[1]) + if u < 0 or u >= image.shape[1] or v < 0 or v >= image.shape[0]: + continue + image[v, u, :] = self.reduced_colors[i, :] + if self.large_points: + if u > 0 and v > 0 and u < image.shape[1] - 1 and v < image.shape[0] - 1: + for ui in range(-1, 2): + for vi in range(-1, 2): + image[v + vi, u + ui, :] = self.reduced_colors[i, :] + return image + + def segmentation(self, size): + """Create segmentation image using morphology operations.""" + mask = np.zeros(size) + for pt in self.uvds: + u = int(pt[0]) + v = int(pt[1]) + if u < 0 or u >= size[1] or v < 0 or v >= size[0]: + continue + mask[v, u] = 255 + kernel = np.ones((3, 3), np.uint8) + mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) # Converts to float. + mask_dilated = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel) + border = mask_dilated - mask + return mask.astype(np.uint8), border.astype(np.uint8) + + +# Orthogonal procrustes method for two sets of 3D points. +# Also does the initial translation to centroid. +# Should work in degenerate cases: 1 or 2 points. +def ortho_procrustes(p_c, p_s): + """Return R,t of the best estimate transform on point clouds p_c and p_s.""" + # No scaling. Transform is from p_c to p_s, i.e., T * p_c ~= p_s. + # Format of args is numpy array, nx3 or nx4,d, each row a 3D point + + p_c = p_c[:, :3] + p_s = p_s[:, :3] + cm = np.mean(p_c, 0) + pcn = p_c - cm # get mean of each dimension, subtract + sm = np.mean(p_s, 0) + psn = p_s - sm + a_mat = psn.transpose().dot(pcn) + u, _, vt = np.linalg.svd(a_mat) + dd = np.eye(3) + dd[2, 2] = np.linalg.det(u.dot(vt)) + rot = u.dot(dd.dot(vt)) # Should check for orthogonality. + t = sm - rot.dot(cm) + tfm = np.eye(4) + tfm[0:3, 0:3] = rot + tfm[0:3, 3] = t + return tfm + + +# Read in CAD model from a .obj file. +# is a file path from the data_tools/objects/ directory. +# is the number of points to use in the reduced set. +def read_mesh(path, num=300): + obj = MeshObj() + print('Reading obj file %s' % path) + obj.read_obj(path, num) + print('Obj file has %d vertices and %d keypoints' % + (len(obj.vertices), len(obj.keypoints))) + return obj + + +# Error functions. +def project(tmat, tvec, tvec_transpose=False): + """Projects homogeneous 3D XYZ coordinates to image uvd coordinates.""" + # has shape [[N,] batch_size, 4, num_kp] or [batch_size, num_kp, 4]. + # has shape [[N,] batch_size, 4, 4] + # Return has shape [[N,] batch_size, 4, num_kp] + + tp = tf.matmul(tmat, tvec, transpose_b=tvec_transpose) + # Using <3:4> instead of <3> preserves shape. + tp = tp / (tp[Ellipsis, 3:4, :] + 1.0e-10) + return tp + + +def world_error(labels, xyzw): + xyzw = tf.transpose(xyzw, [0, 2, 1]) # [batch, 4, num_kp] + # [batch, 4, num_kp] + gt_world_coords = project(labels['to_world_L'], labels['keys_uvd_L'], True) + sub = xyzw[:, :3, :] - gt_world_coords[:, :3, :] + wd = tf.square(sub) + wd = tf.reduce_sum(wd, axis=[-2]) # [batch, num_kp] result. + wd = tf.sqrt(wd) + return wd # [batch, num_kp] -- Gitee From 980f29f9306e417a0e4384b8e5be35c9096c2e7b Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 16:00:46 +0000 Subject: [PATCH 5/9] keypose_tf2 Signed-off-by: Victory_ztk --- .../configs/bottle_0_t5.yaml | 3 ++ .../configs/default_base.yaml | 30 +++++++++++++++++++ 2 files changed, 33 insertions(+) create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/bottle_0_t5.yaml create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/bottle_0_t5.yaml b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/bottle_0_t5.yaml new file mode 100644 index 000000000..1efe76350 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/bottle_0_t5.yaml @@ -0,0 +1,3 @@ +# Stereo +default_file: configs/default_base +dset_dir: tfrecords/bottle_0_756x424_fl400_t5/tfrecords diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml new file mode 100644 index 000000000..cf63ee832 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml @@ -0,0 +1,30 @@ +# Default parameters +# Stereo, regression uvd +batch_size: 32 +learning_rate: [20000, 70000, 0.001, 0.0005, 0.0005, 0.0001, 0.00001, 0.000005] +steps: 80000 +model_params: + batchnorm: [0.999, 1.0e-8, False] # decay, epsilon, scale + filter_size: 3 + loss_prob: 0.001 + loss_onscreen: 0.0 + loss_kp: 1.0 + loss_kp_step: [0, 200] + loss_proj: 2.5 + loss_proj_step: [5000, 10000] + loss_reg: 0.01 + num_filters: 48 + noise: 4.0 + gamma: [0.8, 1.2] + motion: [0, 0, 0, 0] + blur: [1.0, 4.0] + occ_fraction: 0.2 + rot: [5, 0, 0] + shear: [] + scale: [] + max_dilation: 32 + sym: [0] + use_regress: True + use_stereo: True + dilation_rep: 1 + crop: [180, 120, 30] -- Gitee From d1249a4bc2161d3eec49899abffb3635febd4448 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 16:01:07 +0000 Subject: [PATCH 6/9] keypose_tf2 Signed-off-by: Victory_ztk --- 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b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/data/bottle_0/texture_5_pose_0/000001_L.pbtxt @@ -0,0 +1,315 @@ +kp_target { + transform { + element: -0.75454066 + element: -0.65109288 + element: 0.082136776 + element: 0.35567248 + element: -0.24608562 + element: 0.16468977 + element: -0.95515399 + element: 0.1592131 + element: 0.60836688 + element: -0.7409152 + element: -0.28448972 + element: 0.99051199 + element: 0.0 + element: 0.0 + element: 0.0 + element: 1.0 + } + camera { + fx: 675.61713 + fy: 675.61713 + cx: 632.1181 + cy: 338.28537 + baseline: 0.120007 + resx: 1280.0 + resy: 720.0 + } + keypoints { + u: 417.01191 + v: 412.04999 + x: 0.32579056 + y: 0.57229166 + z: 0.75923024 + visible: 1.0 + } + keypoints { + u: 417.13662 + v: 479.85671 + x: 0.32588799 + y: 0.66646765 + z: 0.78489091 + visible: 1.0 + } +} +proj_targets { + transform { + element: -0.81892949 + element: -0.5620517 + element: 0.11598437 + element: 0.28595689 + element: -0.23223507 + element: 0.1397401 + 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0.041177 0.028836 +v -0.006109 0.040177 0.028836 -- Gitee From c4e135f992c93bf2d0cbb15efcec6b8538750166 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 16:53:12 +0000 Subject: [PATCH 8/9] =?UTF-8?q?=E5=88=A0=E9=99=A4=E6=96=87=E4=BB=B6=20Tens?= =?UTF-8?q?orFlow2/contrib/cv/KEYPOSE=5FID2043=5Ffor=5FTensorflow2/configs?= =?UTF-8?q?/default=5Fbase.yaml?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../configs/default_base.yaml | 30 ------------------- 1 file changed, 30 deletions(-) delete mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml deleted file mode 100644 index cf63ee832..000000000 --- a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# Default parameters -# Stereo, regression uvd -batch_size: 32 -learning_rate: [20000, 70000, 0.001, 0.0005, 0.0005, 0.0001, 0.00001, 0.000005] -steps: 80000 -model_params: - batchnorm: [0.999, 1.0e-8, False] # decay, epsilon, scale - filter_size: 3 - loss_prob: 0.001 - loss_onscreen: 0.0 - loss_kp: 1.0 - loss_kp_step: [0, 200] - loss_proj: 2.5 - loss_proj_step: [5000, 10000] - loss_reg: 0.01 - num_filters: 48 - noise: 4.0 - gamma: [0.8, 1.2] - motion: [0, 0, 0, 0] - blur: [1.0, 4.0] - occ_fraction: 0.2 - rot: [5, 0, 0] - shear: [] - scale: [] - max_dilation: 32 - sym: [0] - use_regress: True - use_stereo: True - dilation_rep: 1 - crop: [180, 120, 30] -- Gitee From 0953ed88d54fe83aa01c1829f34df5354d83e189 Mon Sep 17 00:00:00 2001 From: Victory_ztk Date: Tue, 27 Sep 2022 16:53:27 +0000 Subject: [PATCH 9/9] keypose_tf2 Signed-off-by: Victory_ztk --- .../configs/default_base.yaml | 30 +++++++++++++++++++ 1 file changed, 30 insertions(+) create mode 100644 TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml diff --git a/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml new file mode 100644 index 000000000..4f6c79d68 --- /dev/null +++ b/TensorFlow2/contrib/cv/KEYPOSE_ID2043_for_Tensorflow2/configs/default_base.yaml @@ -0,0 +1,30 @@ +# Default parameters +# Stereo, regression uvd +batch_size: 32 +learning_rate: [20000, 70000, 0.001, 0.0005, 0.0005, 0.0001, 0.00001, 0.000005] +steps: 70000 +model_params: + batchnorm: [0.999, 1.0e-8, False] # decay, epsilon, scale + filter_size: 3 + loss_prob: 0.001 + loss_onscreen: 0.0 + loss_kp: 1.0 + loss_kp_step: [0, 200] + loss_proj: 2.5 + loss_proj_step: [5000, 10000] + loss_reg: 0.01 + num_filters: 48 + noise: 4.0 + gamma: [0.8, 1.2] + motion: [0, 0, 0, 0] + blur: [1.0, 4.0] + occ_fraction: 0.2 + rot: [5, 0, 0] + shear: [] + scale: [] + max_dilation: 32 + sym: [0] + use_regress: True + use_stereo: True + dilation_rep: 1 + crop: [180, 120, 30] -- Gitee

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