diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/.gitignore b/TensorFlow/contrib/cv/DifferentiableBinarization/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e9dac7c01a33f77db79c178129fe23abc617bb67 --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/.gitignore @@ -0,0 +1,132 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ +.idea + +/datasets/total_text/ diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/README.md b/TensorFlow/contrib/cv/DifferentiableBinarization/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e9b2922b83e30bb74c82d6fb52c15f573922ed32 --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/README.md @@ -0,0 +1,170 @@ +- [基本信息](#基本信息.md) +- [概述](#概述.md) +- [训练环境准备](#训练环境准备.md) +- [快速上手](#快速上手.md) +- [高级参考](#高级参考.md) +

基本信息

+ +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）：Object Detection** + +**版本（Version）：1.1** + +**修改时间（Modified） ：2022.11.29** + +**大小（Size）：450KB** + +**框架（Framework）：TensorFlow-gpu_1.14.0** + +**模型格式（Model Format）：ckpt** + +**精度（Precision）：Mixed** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Official** + +**描述（Description）：文字检测算法** + +

概述

+ +## 简述 + +基于keras和tensorflow实现的使用可微分二值化实现的实时文本检测算法，主要参考了官方实现MhLiao/DB。使用了“可微分二值化”模块，该模块在分割网络中进行自适应像素二值化过程，简化了后处理，提高了文本检测。 + +- 参考论文： + + [https://arxiv.org/abs/1911.08947](Real-time Scene Text Detection with Differentiable Binarization) + +- 参考实现： + + https://github.com/xuannianz/DifferentiableBinarization + +- 适配昇腾 AI 处理器的实现： + + https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/DifferentiableBinarization + +- 通过Git获取对应commit\_id的代码方法如下： + + git clone {repository_url} # 克隆仓库的代码 + cd {repository_name} # 切换到模型的代码仓目录 + git checkout {branch} # 切换到对应分支 + git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id + cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 + + +## 默认配置 + +- 训练超参（单卡）： + - Batch size: 16 + - steps_per_epoch：200 + - epochs:100 + - dataset_dir: ./datasets/total_text + - checkpoints_dir: ./checkpoints + - start_learning_rate: 1e-3 + - beta_1(一阶矩估计的指数衰减率):0.9 + - beta_2(二阶矩估计的指数衰减率):0.999 + - epsilon(模糊因子):None + - decay=0 + + + +

训练环境准备

+ +- 硬件环境和运行环境准备请参见《[CANN软件安装指南](https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373?category=installation-update)》 +- 运行以下命令安装依赖。 +``` +pip3 install requirements.txt +``` +说明：依赖配置文件requirements.txt文件位于模型的根目录 + +

快速上手

+ +## 数据集准备 + +数据集路径 + datasets/total_text/train_images + datasets/total_text/train_gts + datasets/total_text/train_list.txt + datasets/total_text/test_images + datasets/total_text/test_gts + datasets/total_text/test_list.txt + + +## 模型训练 + +- 运行train.py文件 +- 开始训练。 + + - 启动训练之前，首先要配置程序运行相关环境变量。 + + 环境变量配置信息参见： + + [Ascend 910训练平台环境变量设置](https://gitee.com/ascend/ModelZoo-TensorFlow/wikis/01.%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC%E8%BF%81%E7%A7%BB%E6%A1%88%E4%BE%8B/Ascend%20910%E8%AE%AD%E7%BB%83%E5%B9%B3%E5%8F%B0%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F%E8%AE%BE%E7%BD%AE) + + - 单卡训练 + + 1.首先在脚本train.py中，配置训练数据集路径，模型存储位置，batch_size,请用户根据实际配置，数据集参数如下所示： + + ``` + + --dataset_dir=datasets/total_text --checkpoints_dir=checkpoints --batch_size=16 + + ``` + 2.配置start_learning_rate、beta_1、beta_2、epsilon、decay，在train.py中optimizers.Adam方法中进行配置； + + 3.启动训练 + + 启动单卡训练 （脚本为train.py） + + ``` + python3 train.py + + ``` +## 测试 +`python inference.py` + +![image1](test/img192.jpg) +![image2](test/img795.jpg) +![image3](test/img1095.jpg) + + +

高级参考

+ +## 脚本和示例代码 + +``` +|--README.md #说明文档 +|--train.py #训练代码 +|--generator.py +|--losses.py +|--model.py +|--requirements.txt #所需依赖 +|--transform.py +|--datasets #训练需要的数据集 +| |--test_gts +| |--test_images +| |--train_gts +| |--train_images +| |--train_list.txt +| |--train.txt +|--test +| |--img192.jpg +| |--img795.jpg +| |--img1095.jpg +|--checkpoints #模型存放位置 +``` + +## 脚本参数 + +``` +--dataset_dir=datasets/total_text +--checkpoints_dir=checkpoints +--batch_size=16 +``` + +## 训练过程 + +通过“模型训练”中的训练指令启动单卡或者多卡训练。 + diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/datasets/total_text/test_gts/IMG_0059.JPG.txt b/TensorFlow/contrib/cv/DifferentiableBinarization/datasets/total_text/test_gts/IMG_0059.JPG.txt new file mode 100644 index 0000000000000000000000000000000000000000..67f27bbe4bf3dfebde0eb834f159719004cec311 --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/datasets/total_text/test_gts/IMG_0059.JPG.txt @@ -0,0 +1,2 @@ +303,642,1534,708,1528,819,297,753,0 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a/TensorFlow/contrib/cv/DifferentiableBinarization/generator.py b/TensorFlow/contrib/cv/DifferentiableBinarization/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..6f45c58cd8564a2b222ab499025938a010e22bfb --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/generator.py @@ -0,0 +1,202 @@ +import cv2 +import imgaug.augmenters as iaa +import math +import numpy as np +import os.path as osp +import pyclipper +from shapely.geometry import Polygon + +from transform import transform, crop, resize + +mean = [103.939, 116.779, 123.68] + + +def load_all_anns(gt_paths, dataset='total_text'): + res = [] + for gt in gt_paths: + lines = [] + reader = open(gt, 'r').readlines() + for line in reader: + item = {} + parts = line.strip().split(',') + label = parts[-1] + if label == '1': + label = '###' + line = [i.strip('\ufeff').strip('\xef\xbb\xbf') for i in parts] + if 'icdar' == dataset: + poly = np.array(list(map(float, line[:8]))).reshape((-1, 2)).tolist() + else: + num_points = math.floor((len(line) - 1) / 2) * 2 + poly = np.array(list(map(float, line[:num_points]))).reshape((-1, 2)).tolist() + if len(poly) < 3: + continue + item['poly'] = poly + item['text'] = label + lines.append(item) + res.append(lines) + return res + + +def show_polys(image, anns, window_name): + for ann in anns: + poly = np.array(ann['poly']).astype(np.int32) + cv2.drawContours(image, np.expand_dims(poly, axis=0), -1, (0, 255, 0), 2) + + cv2.namedWindow(window_name, cv2.WINDOW_NORMAL) + cv2.imshow(window_name, image) + + +def draw_thresh_map(polygon, canvas, mask, shrink_ratio=0.4): + polygon = np.array(polygon) + assert polygon.ndim == 2 + assert polygon.shape[1] == 2 + + polygon_shape = Polygon(polygon) + distance = polygon_shape.area * (1 - np.power(shrink_ratio, 2)) / polygon_shape.length + subject = [tuple(l) for l in polygon] + padding = pyclipper.PyclipperOffset() + padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) + padded_polygon = np.array(padding.Execute(distance)[0]) + cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0) + + xmin = padded_polygon[:, 0].min() + xmax = padded_polygon[:, 0].max() + ymin = padded_polygon[:, 1].min() + ymax = padded_polygon[:, 1].max() + width = xmax - xmin + 1 + height = ymax - ymin + 1 + + polygon[:, 0] = polygon[:, 0] - xmin + polygon[:, 1] = polygon[:, 1] - ymin + + xs = np.broadcast_to(np.linspace(0, width - 1, num=width).reshape(1, width), (height, width)) + ys = np.broadcast_to(np.linspace(0, height - 1, num=height).reshape(height, 1), (height, width)) + + + + + + distance_map = np.zeros((polygon.shape[0], height, width), dtype=np.float32) + for i in range(polygon.shape[0]): + j = (i + 1) % polygon.shape[0] + absolute_distance = compute_distance(xs, ys, polygon[i], polygon[j]) + distance_map[i] = np.clip(absolute_distance / distance, 0, 1) + distance_map = np.min(distance_map, axis=0) + + xmin_valid = min(max(0, xmin), canvas.shape[1] - 1) + xmax_valid = min(max(0, xmax), canvas.shape[1] - 1) + ymin_valid = min(max(0, ymin), canvas.shape[0] - 1) + ymax_valid = min(max(0, ymax), canvas.shape[0] - 1) + canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1] = np.fmax( + 1 - distance_map[ + ymin_valid - ymin:ymax_valid - ymax + height, + xmin_valid - xmin:xmax_valid - xmax + width], + canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1]) + + +def compute_distance(xs, ys, point_1, point_2): + square_distance_1 = np.square(xs - point_1[0]) + np.square(ys - point_1[1]) + square_distance_2 = np.square(xs - point_2[0]) + np.square(ys - point_2[1]) + + square_distance = np.square(point_1[0] - point_2[0]) + np.square(point_1[1] - point_2[1]) + + cosin = (square_distance - square_distance_1 - square_distance_2) / (2 * np.sqrt(square_distance_1 * square_distance_2)) + square_sin = 1 - np.square(cosin) + square_sin = np.nan_to_num(square_sin) + result = np.sqrt(square_distance_1 * square_distance_2 * square_sin / square_distance) + + result[cosin < 0] = np.sqrt(np.fmin(square_distance_1, square_distance_2))[cosin < 0] + return result + + +def generate(data_dir, batch_size=16, image_size=640, min_text_size=8, shrink_ratio=0.4, thresh_min=0.3, + thresh_max=0.7, is_training=True): + split = 'train' if is_training else 'test' + with open(osp.join(data_dir, f'{split}_list.txt')) as f: + image_fnames = f.readlines() + image_paths = [osp.join(data_dir, f'{split}_images', image_fname.strip()) for image_fname in image_fnames] + gt_paths = [osp.join(data_dir, f'{split}_gts', image_fname.strip() + '.txt') for image_fname in image_fnames] + all_anns = load_all_anns(gt_paths) + transform_aug = iaa.Sequential([iaa.Fliplr(0.5), iaa.Affine(rotate=(-10, 10)), iaa.Resize((0.5, 3.0))]) + dataset_size = len(image_paths) + indices = np.arange(dataset_size) + if is_training: + np.random.shuffle(indices) + current_idx = 0 + b = 0 + while True: + if current_idx >= dataset_size: + if is_training: + np.random.shuffle(indices) + current_idx = 0 + if b == 0: + # Init batch arrays + batch_images = np.zeros([batch_size, image_size, image_size, 3], dtype=np.float32) + batch_gts = np.zeros([batch_size, image_size, image_size], dtype=np.float32) + batch_masks = np.zeros([batch_size, image_size, image_size], dtype=np.float32) + batch_thresh_maps = np.zeros([batch_size, image_size, image_size], dtype=np.float32) + batch_thresh_masks = np.zeros([batch_size, image_size, image_size], dtype=np.float32) + batch_loss = np.zeros([batch_size, ], dtype=np.float32) + i = indices[current_idx] + image_path = image_paths[i] + anns = all_anns[i] + image = cv2.imread(image_path) + # show_polys(image.copy(), anns, 'before_aug') + if is_training: + transform_aug = transform_aug.to_deterministic() + image, anns = transform(transform_aug, image, anns) + image, anns = crop(image, anns) + image, anns = resize(image_size, image, anns) + # show_polys(image.copy(), anns, 'after_aug') + # cv2.waitKey(0) + anns = [ann for ann in anns if Polygon(ann['poly']).is_valid] + gt = np.zeros((image_size, image_size), dtype=np.float32) + mask = np.ones((image_size, image_size), dtype=np.float32) + thresh_map = np.zeros((image_size, image_size), dtype=np.float32) + thresh_mask = np.zeros((image_size, image_size), dtype=np.float32) + for ann in anns: + poly = np.array(ann['poly']) + height = max(poly[:, 1]) - min(poly[:, 1]) + width = max(poly[:, 0]) - min(poly[:, 0]) + polygon = Polygon(poly) + # generate gt and mask + if polygon.area < 1 or min(height, width) < min_text_size or ann['text'] == '###': + cv2.fillPoly(mask, poly.astype(np.int32)[np.newaxis, :, :], 0) + continue + else: + distance = polygon.area * (1 - np.power(shrink_ratio, 2)) / polygon.length + subject = [tuple(l) for l in ann['poly']] + padding = pyclipper.PyclipperOffset() + padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) + shrinked = padding.Execute(-distance) + if len(shrinked) == 0: + cv2.fillPoly(mask, poly.astype(np.int32)[np.newaxis, :, :], 0) + continue + else: + shrinked = np.array(shrinked[0]).reshape(-1, 2) + if shrinked.shape[0] > 2 and Polygon(shrinked).is_valid: + cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1) + else: + cv2.fillPoly(mask, poly.astype(np.int32)[np.newaxis, :, :], 0) + continue + # generate thresh map and thresh mask + draw_thresh_map(ann['poly'], thresh_map, thresh_mask, shrink_ratio=shrink_ratio) + thresh_map = thresh_map * (thresh_max - thresh_min) + thresh_min + + image = image.astype(np.float32) + image[..., 0] -= mean[0] + image[..., 1] -= mean[1] + image[..., 2] -= mean[2] + batch_images[b] = image + batch_gts[b] = gt + batch_masks[b] = mask + batch_thresh_maps[b] = thresh_map + batch_thresh_masks[b] = thresh_mask + + b += 1 + current_idx += 1 + if b == batch_size: + inputs = [batch_images, batch_gts, batch_masks, batch_thresh_maps, batch_thresh_masks] + outputs = batch_loss + yield inputs, outputs + b = 0 diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/inference.py b/TensorFlow/contrib/cv/DifferentiableBinarization/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..ce01122dd0d555bae87aca4c051eae355004029c --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/inference.py @@ -0,0 +1,132 @@ +import math +import cv2 +import os.path as osp +import glob +import numpy as np +from shapely.geometry import Polygon +import pyclipper + +from model import dbnet + +#调用训练模型 +def resize_image(image, image_short_side=736): + height, width, _ = image.shape + if height < width: + new_height = image_short_side + new_width = int(math.ceil(new_height / height * width / 32) * 32) + else: + new_width = image_short_side + new_height = int(math.ceil(new_width / width * height / 32) * 32) + resized_img = cv2.resize(image, (new_width, new_height)) + return resized_img + + +def box_score_fast(bitmap, _box): + # 计算 box 包围的区域的平均得分 + h, w = bitmap.shape[:2] + box = _box.copy() + xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1) + xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1) + ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1) + ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + box[:, 0] = box[:, 0] - xmin + box[:, 1] = box[:, 1] - ymin + cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] + + +def unclip(box, unclip_ratio=1.5): + poly = Polygon(box) + distance = poly.area * unclip_ratio / poly.length + offset = pyclipper.PyclipperOffset() + offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) + expanded = np.array(offset.Execute(distance)) + return expanded + + +def get_mini_boxes(contour): + bounding_box = cv2.minAreaRect(contour) + points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) + + index_1, index_2, index_3, index_4 = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_1 = 0 + index_4 = 1 + else: + index_1 = 1 + index_4 = 0 + if points[3][1] > points[2][1]: + index_2 = 2 + index_3 = 3 + else: + index_2 = 3 + index_3 = 2 + + box = [points[index_1], points[index_2], + points[index_3], points[index_4]] + return box, min(bounding_box[1]) + + +def polygons_from_bitmap(pred, bitmap, dest_width, dest_height, max_candidates=100, box_thresh=0.7): + pred = pred[..., 0] + bitmap = bitmap[..., 0] + height, width = bitmap.shape + boxes = [] + scores = [] + + _,contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + + for contour in contours[:max_candidates]: + epsilon = 0.01 * cv2.arcLength(contour, True) + approx = cv2.approxPolyDP(contour, epsilon, True) + points = approx.reshape((-1, 2)) + if points.shape[0] < 4: + continue + score = box_score_fast(pred, points.reshape(-1, 2)) + if box_thresh > score: + continue + if points.shape[0] > 2: + box = unclip(points, unclip_ratio=2.0) + if len(box) > 1: + continue + else: + continue + box = box.reshape(-1, 2) + _, sside = get_mini_boxes(box.reshape((-1, 1, 2))) + if sside < 5: + continue + + box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width) + box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height) + boxes.append(box.tolist()) + scores.append(score) + return boxes, scores + + +if __name__ == '__main__': + mean = np.array([103.939, 116.779, 123.68]) + _, model = dbnet() + """此处调用已经训练好的训练模型""" + model.load_weights('dave/db_48_2.0216_2.5701.h5', by_name=True, skip_mismatch=True) + print(glob.glob(osp.join('datasets/total_text/test_images', '*.JPG'))) + for image_path in glob.glob(osp.join('datasets/total_text/test_images', '*.JPG')): + image = cv2.imread(image_path) + src_image = image.copy() + h, w = image.shape[:2] + image = resize_image(image) + image = image.astype(np.float32) + image -= mean + image_input = np.expand_dims(image, axis=0) + p = model.predict(image_input)[0] + bitmap = p > 0.3 + boxes, scores = polygons_from_bitmap(p, bitmap, w, h, box_thresh=0.5) + for box in boxes: + cv2.drawContours(src_image, [np.array(box)], -1, (0, 255, 0), 2) + cv2.namedWindow('image', cv2.WINDOW_NORMAL) + cv2.imshow('image', src_image) + cv2.waitKey(0) + image_fname = osp.split(image_path)[-1] + cv2.imwrite('test/' + image_fname, src_image) + print('finish') diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/losses.py b/TensorFlow/contrib/cv/DifferentiableBinarization/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..e86c3038b4438e9e41f7d7261cbc5dc465868cfd --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/losses.py @@ -0,0 +1,58 @@ +import tensorflow as tf +import keras.backend as K +"""损失函数""" + +def balanced_crossentropy_loss(args, negative_ratio=3., scale=5.): + pred, gt, mask = args + pred = pred[..., 0] + positive_mask = (gt * mask) + negative_mask = ((1 - gt) * mask) + positive_count = tf.reduce_sum(positive_mask) + negative_count = tf.reduce_min([tf.reduce_sum(negative_mask), positive_count * negative_ratio]) + loss = K.binary_crossentropy(gt, pred) + positive_loss = loss * positive_mask + negative_loss = loss * negative_mask + negative_loss, _ = tf.nn.top_k(tf.reshape(negative_loss, (-1,)), tf.cast(negative_count, tf.int32)) + + balanced_loss = (tf.reduce_sum(positive_loss) + tf.reduce_sum(negative_loss)) / ( + positive_count + negative_count + 1e-6) + balanced_loss = balanced_loss * scale + return balanced_loss, loss + + +def dice_loss(args): + """ + + Args: + pred: (b, h, w, 1) + gt: (b, h, w) + mask: (b, h, w) + weights: (b, h, w) + Returns: + + """ + pred, gt, mask, weights = args + pred = pred[..., 0] + weights = (weights - tf.reduce_min(weights)) / (tf.reduce_max(weights) - tf.reduce_min(weights)) + 1. + mask = mask * weights + intersection = tf.reduce_sum(pred * gt * mask) + union = tf.reduce_sum(pred * mask) + tf.reduce_sum(gt * mask) + 1e-6 + loss = 1 - 2.0 * intersection / union + return loss + + +def l1_loss(args, scale=10.): + pred, gt, mask = args + pred = pred[..., 0] + mask_sum = tf.reduce_sum(mask) + loss = K.switch(mask_sum > 0, tf.reduce_sum(tf.abs(pred - gt) * mask) / mask_sum, tf.constant(0.)) + loss = loss * scale + return loss + + +def db_loss(args): + binary, thresh_binary, gt, mask, thresh, thresh_map, thresh_mask = args + l1_loss_ = l1_loss([thresh, thresh_map, thresh_mask]) + balanced_ce_loss_, dice_loss_weights = balanced_crossentropy_loss([binary, gt, mask]) + dice_loss_ = dice_loss([thresh_binary, gt, mask, dice_loss_weights]) + return l1_loss_ + balanced_ce_loss_ + dice_loss_ diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/model.py b/TensorFlow/contrib/cv/DifferentiableBinarization/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6d3f223b41ef3eb345abfcc414b8f1701bc1aa0e --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/model.py @@ -0,0 +1,70 @@ +from keras_resnet.models import ResNet50 +from keras import layers, models +import tensorflow as tf +from losses import db_loss + +#神经网络的结构--训练模型 +def dbnet(input_size=640, k=50): + image_input = layers.Input(shape=(None, None, 3)) + gt_input = layers.Input(shape=(input_size, input_size)) + mask_input = layers.Input(shape=(input_size, input_size)) + thresh_input = layers.Input(shape=(input_size, input_size)) + thresh_mask_input = layers.Input(shape=(input_size, input_size)) + backbone = ResNet50(inputs=image_input, include_top=False, freeze_bn=True) + C2, C3, C4, C5 = backbone.outputs + in2 = layers.Conv2D(256, (1, 1), padding='same', kernel_initializer='he_normal', name='in2')(C2) + in3 = layers.Conv2D(256, (1, 1), padding='same', kernel_initializer='he_normal', name='in3')(C3) + in4 = layers.Conv2D(256, (1, 1), padding='same', kernel_initializer='he_normal', name='in4')(C4) + in5 = layers.Conv2D(256, (1, 1), padding='same', kernel_initializer='he_normal', name='in5')(C5) + + # 1 / 32 * 8 = 1 / 4 + P5 = layers.UpSampling2D(size=(8, 8))( + layers.Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(in5)) + # 1 / 16 * 4 = 1 / 4 + out4 = layers.Add()([in4, layers.UpSampling2D(size=(2, 2))(in5)]) + P4 = layers.UpSampling2D(size=(4, 4))( + layers.Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(out4)) + # 1 / 8 * 2 = 1 / 4 + out3 = layers.Add()([in3, layers.UpSampling2D(size=(2, 2))(out4)]) + P3 = layers.UpSampling2D(size=(2, 2))( + layers.Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(out3)) + # 1 / 4 + P2 = layers.Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')( + layers.Add()([in2, layers.UpSampling2D(size=(2, 2))(out3)])) + # (b, /4, /4, 256) + fuse = layers.Concatenate()([P2, P3, P4, P5]) + + # probability map + p = layers.Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(fuse) + p = layers.BatchNormalization()(p) + p = layers.ReLU()(p) + p = layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), kernel_initializer='he_normal', use_bias=False)(p) + p = layers.BatchNormalization()(p) + p = layers.ReLU()(p) + p = layers.Conv2DTranspose(1, (2, 2), strides=(2, 2), kernel_initializer='he_normal', + activation='sigmoid')(p) + + # threshold map + t = layers.Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', use_bias=False)(fuse) + t = layers.BatchNormalization()(t) + t = layers.ReLU()(t) + t = layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), kernel_initializer='he_normal', use_bias=False)(t) + t = layers.BatchNormalization()(t) + t = layers.ReLU()(t) + t = layers.Conv2DTranspose(1, (2, 2), strides=(2, 2), kernel_initializer='he_normal', + activation='sigmoid')(t) + + # approximate binary map + b_hat = layers.Lambda(lambda x: 1 / (1 + tf.exp(-k * (x[0] - x[1]))))([p, t]) + + loss = layers.Lambda(db_loss, name='db_loss')([p, b_hat, gt_input, mask_input, t, thresh_input, thresh_mask_input]) + training_model = models.Model(inputs=[image_input, gt_input, mask_input, thresh_input, thresh_mask_input], + outputs=loss) + prediction_model = models.Model(inputs=image_input, outputs=p) + return training_model, prediction_model + + +if __name__ == '__main__': + model, _ = dbnet() + """打出网络结构""" + #model.summary() diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/requirements.txt b/TensorFlow/contrib/cv/DifferentiableBinarization/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..780c636a08627ff627b35c9fafde058f6b20725a --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/requirements.txt @@ -0,0 +1,8 @@ +Keras==2.2.5 +keras-resnet==0.2.0 +opencv-contrib-python==3.4.2.17 +opencv-python==3.4.2.17 +Pillow==6.2.0 +tensorflow-gpu==1.14.0 +progressbar2 +git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/test/img1095.jpg b/TensorFlow/contrib/cv/DifferentiableBinarization/test/img1095.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c7e4e515f0def411d558c8a5c28a797a7f0cbb23 Binary files /dev/null and b/TensorFlow/contrib/cv/DifferentiableBinarization/test/img1095.jpg differ diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/test/img192.jpg b/TensorFlow/contrib/cv/DifferentiableBinarization/test/img192.jpg new file mode 100644 index 0000000000000000000000000000000000000000..dbb678a50681ba8987f8edbecacf9b1da9b6c2ff Binary files /dev/null and b/TensorFlow/contrib/cv/DifferentiableBinarization/test/img192.jpg differ diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/test/img795.jpg b/TensorFlow/contrib/cv/DifferentiableBinarization/test/img795.jpg new file mode 100644 index 0000000000000000000000000000000000000000..83660e736a41c8aec7c6363da9d46e4b17000989 Binary files /dev/null and b/TensorFlow/contrib/cv/DifferentiableBinarization/test/img795.jpg differ diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/train.py b/TensorFlow/contrib/cv/DifferentiableBinarization/train.py new file mode 100644 index 0000000000000000000000000000000000000000..63d255cac5d20e631e33cb106a26c8581e6a6496 --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/train.py @@ -0,0 +1,43 @@ +import datetime +import os.path as osp +from keras import callbacks +from keras import optimizers +from keras.utils import get_file +import os +from generator import generate +from model import dbnet +today = datetime.date.today() +checkpoints_dir = f'checkpoints/{today}' + +batch_size = 16 + +if not osp.exists(checkpoints_dir): + os.makedirs(checkpoints_dir) + +train_generator = generate('datasets/total_text', batch_size=batch_size, is_training=True) +val_generator = generate('datasets/total_text', batch_size=batch_size, is_training=False) + +model, prediction_model = dbnet() +resnet_filename = 'ResNet-50-model.keras.h5' +resnet_resource = 'https://github.com/fizyr/keras-models/releases/download/v0.0.1/{}'.format(resnet_filename) +resnet_filepath = get_file(resnet_filename, resnet_resource, cache_subdir='models', + md5_hash='3e9f4e4f77bbe2c9bec13b53ee1c2319') +model.load_weights(resnet_filepath, by_name=True, skip_mismatch=True) +model.compile(optimizer=optimizers.Adam(lr=1e-3), loss={'db_loss': lambda y_true, y_pred: y_pred}) +checkpoint = callbacks.ModelCheckpoint( + osp.join(checkpoints_dir, 'db_{epoch:02d}_{loss:.4f}_{val_loss:.4f}.h5'), + verbose=1, + mode="min", +) +"""开始训练""" +model.fit_generator( + generator=train_generator, + steps_per_epoch=200, + initial_epoch=0, + epochs=100, + verbose=1, + callbacks=[checkpoint], + validation_data=val_generator, + validation_steps=19 +) + diff --git a/TensorFlow/contrib/cv/DifferentiableBinarization/transform.py b/TensorFlow/contrib/cv/DifferentiableBinarization/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..d1e89636d42ba98157ccf315aa7460ea47310957 --- /dev/null +++ b/TensorFlow/contrib/cv/DifferentiableBinarization/transform.py @@ -0,0 +1,120 @@ +import imgaug +import cv2 +import numpy as np + + +def transform(aug, image, anns): + image_shape = image.shape + image = aug.augment_image(image) + new_anns = [] + for ann in anns: + keypoints = [imgaug.Keypoint(p[0], p[1]) for p in ann['poly']] + keypoints = aug.augment_keypoints( + [imgaug.KeypointsOnImage(keypoints, shape=image_shape)])[0].keypoints + poly = [(min(max(0, p.x), image.shape[1] - 1), min(max(0, p.y), image.shape[0] - 1)) for p in keypoints] + new_ann = {'poly': poly, 'text': ann['text']} + new_anns.append(new_ann) + return image, new_anns + + +def split_regions(axis): + regions = [] + min_axis_index = 0 + for i in range(1, axis.shape[0]): + if axis[i] != axis[i - 1] + 1: + region = axis[min_axis_index:i] + min_axis_index = i + regions.append(region) + return regions + + +def random_select(axis): + xx = np.random.choice(axis, size=2) + xmin = np.min(xx) + xmax = np.max(xx) + return xmin, xmax + + +def region_wise_random_select(regions): + selected_index = list(np.random.choice(len(regions), 2)) + selected_values = [] + for index in selected_index: + axis = regions[index] + xx = int(np.random.choice(axis, size=1)) + selected_values.append(xx) + xmin = min(selected_values) + xmax = max(selected_values) + return xmin, xmax + + +def crop(image, anns, max_tries=10, min_crop_side_ratio=0.1): + h, w, _ = image.shape + h_array = np.zeros(h, dtype=np.int32) + w_array = np.zeros(w, dtype=np.int32) + for ann in anns: + points = np.round(ann['poly'], decimals=0).astype(np.int32) + minx = np.min(points[:, 0]) + maxx = np.max(points[:, 0]) + w_array[minx:maxx] = 1 + miny = np.min(points[:, 1]) + maxy = np.max(points[:, 1]) + h_array[miny:maxy] = 1 + # ensure the cropped area not across a text + h_axis = np.where(h_array == 0)[0] + w_axis = np.where(w_array == 0)[0] + + if len(h_axis) == 0 or len(w_axis) == 0: + return image, anns + + h_regions = split_regions(h_axis) + w_regions = split_regions(w_axis) + + for i in range(max_tries): + if len(w_regions) > 1: + xmin, xmax = region_wise_random_select(w_regions) + else: + xmin, xmax = random_select(w_axis) + if len(h_regions) > 1: + ymin, ymax = region_wise_random_select(h_regions) + else: + ymin, ymax = random_select(h_axis) + + if xmax - xmin < min_crop_side_ratio * w or ymax - ymin < min_crop_side_ratio * h: + # area too small + continue + new_anns = [] + for ann in anns: + poly = np.array(ann['poly']) + if not (poly[:, 0].min() > xmax + or poly[:, 0].max() < xmin + or poly[:, 1].min() > ymax + or poly[:, 1].max() < ymin): + poly[:, 0] -= xmin + poly[:, 0] = np.clip(poly[:, 0], 0., (xmax - xmin - 1) * 1.) + poly[:, 1] -= ymin + poly[:, 1] = np.clip(poly[:, 1], 0., (ymax - ymin - 1) * 1.) + new_ann = {'poly': poly.tolist(), 'text': ann['text']} + new_anns.append(new_ann) + + if len(new_anns) > 0: + return image[ymin:ymax, xmin:xmax], new_anns + + return image, anns + + +def resize(size, image, anns): + h, w, c = image.shape + scale_w = size / w + scale_h = size / h + scale = min(scale_w, scale_h) + h = int(h * scale) + w = int(w * scale) + padimg = np.zeros((size, size, c), image.dtype) + padimg[:h, :w] = cv2.resize(image, (w, h)) + new_anns = [] + for ann in anns: + poly = np.array(ann['poly']).astype(np.float64) + poly *= scale + new_ann = {'poly': poly.tolist(), 'text': ann['text']} + new_anns.append(new_ann) + return padimg, new_anns