# coding: utf8 # copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # # 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 __future__ import print_function import cv2 import numpy as np from utils.config import cfg from models.model_builder import ModelPhase def resize(img, grt=None, mode=ModelPhase.TRAIN): """ 改变图像及标签图像尺寸 AUG.AUG_METHOD为unpadding，所有模式均直接resize到AUG.FIX_RESIZE_SIZE的尺寸 AUG.AUG_METHOD为stepscaling, 按比例resize，训练时比例范围AUG.MIN_SCALE_FACTOR到AUG.MAX_SCALE_FACTOR,间隔为AUG.SCALE_STEP_SIZE，其他模式返回原图 AUG.AUG_METHOD为rangescaling，长边对齐，短边按比例变化，训练时长边对齐范围AUG.MIN_RESIZE_VALUE到AUG.MAX_RESIZE_VALUE，其他模式长边对齐AUG.INF_RESIZE_VALUE Args： img(numpy.ndarray): 输入图像 grt(numpy.ndarray): 标签图像，默认为None mode(string): 模式, 默认训练模式，即ModelPhase.TRAIN Returns： resize后的图像和标签图 """ if cfg.AUG.AUG_METHOD == 'unpadding': target_size = cfg.AUG.FIX_RESIZE_SIZE img = cv2.resize(img, target_size, interpolation=cv2.INTER_LINEAR) if grt is not None: grt = cv2.resize(grt, target_size, interpolation=cv2.INTER_NEAREST) elif cfg.AUG.AUG_METHOD == 'stepscaling': if mode == ModelPhase.TRAIN: min_scale_factor = cfg.AUG.MIN_SCALE_FACTOR max_scale_factor = cfg.AUG.MAX_SCALE_FACTOR step_size = cfg.AUG.SCALE_STEP_SIZE scale_factor = get_random_scale(min_scale_factor, max_scale_factor, step_size) img, grt = randomly_scale_image_and_label( img, grt, scale=scale_factor) elif cfg.AUG.AUG_METHOD == 'rangescaling': min_resize_value = cfg.AUG.MIN_RESIZE_VALUE max_resize_value = cfg.AUG.MAX_RESIZE_VALUE if mode == ModelPhase.TRAIN: if min_resize_value == max_resize_value: random_size = min_resize_value else: random_size = int( np.random.uniform(min_resize_value, max_resize_value) + 0.5) else: random_size = cfg.AUG.INF_RESIZE_VALUE value = max(img.shape[0], img.shape[1]) scale = float(random_size) / float(value) img = cv2.resize( img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_LINEAR) if grt is not None: grt = cv2.resize( grt, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_NEAREST) else: raise Exception("Unexpect data augmention method: {}".format( cfg.AUG.AUG_METHOD)) return img, grt def get_random_scale(min_scale_factor, max_scale_factor, step_size): """ 在一定范围内得到随机值，范围为min_scale_factor到max_scale_factor，间隔为step_size Args： min_scale_factor(float): 随机尺度下限，大于0 max_scale_factor(float): 随机尺度上限，不小于下限值 step_size(float): 尺度间隔，非负, 等于为0时直接返回min_scale_factor到max_scale_factor范围内任一值 Returns： 随机尺度值 """ if min_scale_factor < 0 or min_scale_factor > max_scale_factor: raise ValueError('Unexpected value of min_scale_factor.') if min_scale_factor == max_scale_factor: return min_scale_factor if step_size == 0: return np.random.uniform(min_scale_factor, max_scale_factor) num_steps = int((max_scale_factor - min_scale_factor) / step_size + 1) scale_factors = np.linspace(min_scale_factor, max_scale_factor, num_steps).tolist() np.random.shuffle(scale_factors) return scale_factors[0] def randomly_scale_image_and_label(image, label=None, scale=1.0): """ 按比例resize图像和标签图, 如果scale为1，返回原图 Args： image(numpy.ndarray): 输入图像 label(numpy.ndarray): 标签图，默认None sclae(float): 图片resize的比例，非负，默认1.0 Returns： resize后的图像和标签图 """ if scale == 1.0: return image, label height = image.shape[0] width = image.shape[1] new_height = int(height * scale + 0.5) new_width = int(width * scale + 0.5) new_image = cv2.resize( image, (new_width, new_height), interpolation=cv2.INTER_LINEAR) if label is not None: height = label.shape[0] width = label.shape[1] new_height = int(height * scale + 0.5) new_width = int(width * scale + 0.5) new_label = cv2.resize( label, (new_width, new_height), interpolation=cv2.INTER_NEAREST) return new_image, new_label def random_rotation(crop_img, crop_seg, rich_crop_max_rotation, mean_value): """ 随机旋转图像和标签图 Args： crop_img(numpy.ndarray): 输入图像 crop_seg(numpy.ndarray): 标签图 rich_crop_max_rotation(int)：旋转最大角度，0-90 mean_value(list)：均值, 对图片旋转产生的多余区域使用均值填充 Returns： 旋转后的图像和标签图 """ ignore_index = cfg.DATASET.IGNORE_INDEX if rich_crop_max_rotation > 0: (h, w) = crop_img.shape[:2] do_rotation = np.random.uniform(-rich_crop_max_rotation, rich_crop_max_rotation) pc = (w // 2, h // 2) r = cv2.getRotationMatrix2D(pc, do_rotation, 1.0) cos = np.abs(r[0, 0]) sin = np.abs(r[0, 1]) nw = int((h * sin) + (w * cos)) nh = int((h * cos) + (w * sin)) (cx, cy) = pc r[0, 2] += (nw / 2) - cx r[1, 2] += (nh / 2) - cy dsize = (nw, nh) crop_img = cv2.warpAffine( crop_img, r, dsize=dsize, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=mean_value) crop_seg = cv2.warpAffine( crop_seg, r, dsize=dsize, flags=cv2.INTER_NEAREST, borderMode=cv2.BORDER_CONSTANT, borderValue=(ignore_index, ignore_index, ignore_index)) return crop_img, crop_seg def rand_scale_aspect(crop_img, crop_seg, rich_crop_min_scale=0, rich_crop_aspect_ratio=0): """ 从输入图像和标签图像中裁取随机宽高比的图像，并reszie回原始尺寸 Args: crop_img(numpy.ndarray): 输入图像 crop_seg(numpy.ndarray): 标签图像 rich_crop_min_scale(float)：裁取图像占原始图像的面积比，0-1，默认0返回原图 rich_crop_aspect_ratio(float): 裁取图像的宽高比范围，非负，默认0返回原图 Returns: 裁剪并resize回原始尺寸的图像和标签图像 """ if rich_crop_min_scale == 0 or rich_crop_aspect_ratio == 0: return crop_img, crop_seg else: img_height = crop_img.shape[0] img_width = crop_img.shape[1] for i in range(0, 10): area = img_height * img_width target_area = area * np.random.uniform(rich_crop_min_scale, 1.0) aspectRatio = np.random.uniform(rich_crop_aspect_ratio, 1.0 / rich_crop_aspect_ratio) dw = int(np.sqrt(target_area * 1.0 * aspectRatio)) dh = int(np.sqrt(target_area * 1.0 / aspectRatio)) if (np.random.randint(10) < 5): tmp = dw dw = dh dh = tmp if (dh < img_height and dw < img_width): h1 = np.random.randint(0, img_height - dh) w1 = np.random.randint(0, img_width - dw) crop_img = crop_img[h1:(h1 + dh), w1:(w1 + dw), :] crop_seg = crop_seg[h1:(h1 + dh), w1:(w1 + dw)] crop_img = cv2.resize( crop_img, (img_width, img_height), interpolation=cv2.INTER_LINEAR) crop_seg = cv2.resize( crop_seg, (img_width, img_height), interpolation=cv2.INTER_NEAREST) break return crop_img, crop_seg def saturation_jitter(cv_img, jitter_range): """ 调节图像饱和度 Args: cv_img(numpy.ndarray): 输入图像 jitter_range(float): 调节程度，0-1 Returns: 饱和度调整后的图像 """ greyMat = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY) greyMat = greyMat[:, :, None] * np.ones(3, dtype=int)[None, None, :] cv_img = cv_img.astype(np.float32) cv_img = cv_img * (1 - jitter_range) + jitter_range * greyMat cv_img = np.where(cv_img > 255, 255, cv_img) cv_img = cv_img.astype(np.uint8) return cv_img def brightness_jitter(cv_img, jitter_range): """ 调节图像亮度 Args: cv_img(numpy.ndarray): 输入图像 jitter_range(float): 调节程度，0-1 Returns: 亮度调整后的图像 """ cv_img = cv_img.astype(np.float32) cv_img = cv_img * (1.0 - jitter_range) cv_img = np.where(cv_img > 255, 255, cv_img) cv_img = cv_img.astype(np.uint8) return cv_img def contrast_jitter(cv_img, jitter_range): """ 调节图像对比度 Args: cv_img(numpy.ndarray): 输入图像 jitter_range(float): 调节程度，0-1 Returns: 对比度调整后的图像 """ greyMat = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY) mean = np.mean(greyMat) cv_img = cv_img.astype(np.float32) cv_img = cv_img * (1 - jitter_range) + jitter_range * mean cv_img = np.where(cv_img > 255, 255, cv_img) cv_img = cv_img.astype(np.uint8) return cv_img def random_jitter(cv_img, saturation_range, brightness_range, contrast_range): """ 图像亮度、饱和度、对比度调节，在调整范围内随机获得调节比例，并随机顺序叠加三种效果 Args: cv_img(numpy.ndarray): 输入图像 saturation_range(float): 饱和对调节范围，0-1 brightness_range(float): 亮度调节范围，0-1 contrast_range(float): 对比度调节范围，0-1 Returns: 亮度、饱和度、对比度调整后图像 """ saturation_ratio = np.random.uniform(-saturation_range, saturation_range) brightness_ratio = np.random.uniform(-brightness_range, brightness_range) contrast_ratio = np.random.uniform(-contrast_range, contrast_range) order = [1, 2, 3] np.random.shuffle(order) for i in range(3): if order[i] == 0: cv_img = saturation_jitter(cv_img, saturation_ratio) if order[i] == 1: cv_img = brightness_jitter(cv_img, brightness_ratio) if order[i] == 2: cv_img = contrast_jitter(cv_img, contrast_ratio) return cv_img def hsv_color_jitter(crop_img, brightness_jitter_ratio=0, saturation_jitter_ratio=0, contrast_jitter_ratio=0): """ 图像亮度、饱和度、对比度调节 Args: crop_img(numpy.ndarray): 输入图像 brightness_jitter_ratio(float): 亮度调节度最大值，1-0，默认0 saturation_jitter_ratio(float): 饱和度调节度最大值，1-0，默认0 contrast_jitter_ratio(float): 对比度调节度最大值，1-0，默认0 Returns： 亮度、饱和度、对比度调节后图像 """ if brightness_jitter_ratio > 0 or \ saturation_jitter_ratio > 0 or \ contrast_jitter_ratio > 0: random_jitter(crop_img, saturation_jitter_ratio, brightness_jitter_ratio, contrast_jitter_ratio) return crop_img def rand_crop(crop_img, crop_seg, mode=ModelPhase.TRAIN): """ 随机裁剪图片和标签图, 若crop尺寸大于原始尺寸，分别使用均值和ignore值填充再进行crop， crop尺寸与原始尺寸一致，返回原图，crop尺寸小于原始尺寸直接crop Args: crop_img(numpy.ndarray): 输入图像 crop_seg(numpy.ndarray): 标签图 mode(string): 模式, 默认训练模式，验证或预测、可视化模式时crop尺寸需大于原始图片尺寸 Returns： 裁剪后的图片和标签图 """ img_height = crop_img.shape[0] img_width = crop_img.shape[1] if ModelPhase.is_train(mode): crop_width = cfg.TRAIN_CROP_SIZE[0] crop_height = cfg.TRAIN_CROP_SIZE[1] else: crop_width = cfg.EVAL_CROP_SIZE[0] crop_height = cfg.EVAL_CROP_SIZE[1] if not ModelPhase.is_train(mode): if (crop_height < img_height or crop_width < img_width): raise Exception( "Crop size({},{}) must large than img size({},{}) when in EvalPhase." .format(crop_width, crop_height, img_width, img_height)) if img_height == crop_height and img_width == crop_width: return crop_img, crop_seg else: pad_height = max(crop_height - img_height, 0) pad_width = max(crop_width - img_width, 0) if (pad_height > 0 or pad_width > 0): crop_img = cv2.copyMakeBorder( crop_img, 0, pad_height, 0, pad_width, cv2.BORDER_CONSTANT, value=cfg.DATASET.PADDING_VALUE) if crop_seg is not None: crop_seg = cv2.copyMakeBorder( crop_seg, 0, pad_height, 0, pad_width, cv2.BORDER_CONSTANT, value=cfg.DATASET.IGNORE_INDEX) img_height = crop_img.shape[0] img_width = crop_img.shape[1] if crop_height > 0 and crop_width > 0: h_off = np.random.randint(img_height - crop_height + 1) w_off = np.random.randint(img_width - crop_width + 1) crop_img = crop_img[h_off:(crop_height + h_off), w_off:( w_off + crop_width), :] if crop_seg is not None: crop_seg = crop_seg[h_off:(crop_height + h_off), w_off:( w_off + crop_width)] return crop_img, crop_seg