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| # -*- coding: utf-8 -*- | |
| import numpy as np | |
| import cv2 | |
| import random | |
| import math | |
| def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): | |
| """change color hue, saturation, value""" | |
| r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains | |
| hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) | |
| dtype = img.dtype # uint8 | |
| x = np.arange(0, 256, dtype=np.int16) | |
| lut_hue = ((x * r[0]) % 180).astype(dtype) | |
| lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) | |
| lut_val = np.clip(x * r[2], 0, 255).astype(dtype) | |
| img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) | |
| cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed | |
| # Histogram equalization | |
| # if random.random() < 0.2: | |
| # for i in range(3): | |
| # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) | |
| def random_perspective(combination, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): | |
| """combination of img transform""" | |
| # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) | |
| # targets = [cls, xyxy] | |
| img, gray, line = combination | |
| height = img.shape[0] + border[0] * 2 # shape(h,w,c) | |
| width = img.shape[1] + border[1] * 2 | |
| # Center | |
| C = np.eye(3) | |
| C[0, 2] = -img.shape[1] / 2 # x translation (pixels) | |
| C[1, 2] = -img.shape[0] / 2 # y translation (pixels) | |
| # Perspective | |
| P = np.eye(3) | |
| P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) | |
| P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) | |
| # Rotation and Scale | |
| R = np.eye(3) | |
| a = random.uniform(-degrees, degrees) | |
| # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations | |
| s = random.uniform(1 - scale, 1 + scale) | |
| # s = 2 ** random.uniform(-scale, scale) | |
| R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) | |
| # Shear | |
| S = np.eye(3) | |
| S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) | |
| S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) | |
| # Translation | |
| T = np.eye(3) | |
| T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) | |
| T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) | |
| # Combined rotation matrix | |
| M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT | |
| if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed | |
| if perspective: | |
| img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) | |
| gray = cv2.warpPerspective(gray, M, dsize=(width, height), borderValue=0) | |
| line = cv2.warpPerspective(line, M, dsize=(width, height), borderValue=0) | |
| else: # affine | |
| img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) | |
| gray = cv2.warpAffine(gray, M[:2], dsize=(width, height), borderValue=0) | |
| line = cv2.warpAffine(line, M[:2], dsize=(width, height), borderValue=0) | |
| # Visualize | |
| # import matplotlib.pyplot as plt | |
| # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() | |
| # ax[0].imshow(img[:, :, ::-1]) # base | |
| # ax[1].imshow(img2[:, :, ::-1]) # warped | |
| # Transform label coordinates | |
| n = len(targets) | |
| if n: | |
| # warp points | |
| xy = np.ones((n * 4, 3)) | |
| xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 | |
| xy = xy @ M.T # transform | |
| if perspective: | |
| xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale | |
| else: # affine | |
| xy = xy[:, :2].reshape(n, 8) | |
| # create new boxes | |
| x = xy[:, [0, 2, 4, 6]] | |
| y = xy[:, [1, 3, 5, 7]] | |
| xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T | |
| # # apply angle-based reduction of bounding boxes | |
| # radians = a * math.pi / 180 | |
| # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 | |
| # x = (xy[:, 2] + xy[:, 0]) / 2 | |
| # y = (xy[:, 3] + xy[:, 1]) / 2 | |
| # w = (xy[:, 2] - xy[:, 0]) * reduction | |
| # h = (xy[:, 3] - xy[:, 1]) * reduction | |
| # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T | |
| # clip boxes | |
| xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) | |
| xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) | |
| # filter candidates | |
| i = _box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T) | |
| targets = targets[i] | |
| targets[:, 1:5] = xy[i] | |
| combination = (img, gray, line) | |
| return combination, targets | |
| def cutout(combination, labels): | |
| # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 | |
| image, gray = combination | |
| h, w = image.shape[:2] | |
| def bbox_ioa(box1, box2): | |
| # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 | |
| box2 = box2.transpose() | |
| # Get the coordinates of bounding boxes | |
| b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] | |
| b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] | |
| # Intersection area | |
| inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ | |
| (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) | |
| # box2 area | |
| box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 | |
| # Intersection over box2 area | |
| return inter_area / box2_area | |
| # create random masks | |
| scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction | |
| for s in scales: | |
| mask_h = random.randint(1, int(h * s)) | |
| mask_w = random.randint(1, int(w * s)) | |
| # box | |
| xmin = max(0, random.randint(0, w) - mask_w // 2) | |
| ymin = max(0, random.randint(0, h) - mask_h // 2) | |
| xmax = min(w, xmin + mask_w) | |
| ymax = min(h, ymin + mask_h) | |
| # print('xmin:{},ymin:{},xmax:{},ymax:{}'.format(xmin,ymin,xmax,ymax)) | |
| # apply random color mask | |
| image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] | |
| gray[ymin:ymax, xmin:xmax] = -1 | |
| # return unobscured labels | |
| if len(labels) and s > 0.03: | |
| box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) | |
| ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area | |
| labels = labels[ioa < 0.60] # remove >60% obscured labels | |
| return image, gray, labels | |
| def letterbox(combination, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): | |
| """Resize the input image and automatically padding to suitable shape :https://zhuanlan.zhihu.com/p/172121380""" | |
| # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 | |
| img, gray, line = combination | |
| shape = img.shape[:2] # current shape [height, width] | |
| if isinstance(new_shape, int): | |
| new_shape = (new_shape, new_shape) | |
| # Scale ratio (new / old) | |
| r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) | |
| if not scaleup: # only scale down, do not scale up (for better test mAP) | |
| r = min(r, 1.0) | |
| # Compute padding | |
| ratio = r, r # width, height ratios | |
| new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) | |
| dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding | |
| if auto: # minimum rectangle | |
| dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding | |
| elif scaleFill: # stretch | |
| dw, dh = 0.0, 0.0 | |
| new_unpad = (new_shape[1], new_shape[0]) | |
| ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios | |
| dw /= 2 # divide padding into 2 sides | |
| dh /= 2 | |
| if shape[::-1] != new_unpad: # resize | |
| img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) | |
| gray = cv2.resize(gray, new_unpad, interpolation=cv2.INTER_LINEAR) | |
| line = cv2.resize(line, new_unpad, interpolation=cv2.INTER_LINEAR) | |
| top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) | |
| left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) | |
| img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border | |
| gray = cv2.copyMakeBorder(gray, top, bottom, left, right, cv2.BORDER_CONSTANT, value=0) # add border | |
| line = cv2.copyMakeBorder(line, top, bottom, left, right, cv2.BORDER_CONSTANT, value=0) # add border | |
| # print(img.shape) | |
| combination = (img, gray, line) | |
| return combination, ratio, (dw, dh) | |
| def letterbox_for_img(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): | |
| # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 | |
| shape = img.shape[:2] # current shape [height, width] | |
| if isinstance(new_shape, int): | |
| new_shape = (new_shape, new_shape) | |
| # Scale ratio (new / old) | |
| r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) | |
| if not scaleup: # only scale down, do not scale up (for better test mAP) | |
| r = min(r, 1.0) | |
| # Compute padding | |
| ratio = r, r # width, height ratios | |
| new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) | |
| dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding | |
| if auto: # minimum rectangle | |
| dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding | |
| elif scaleFill: # stretch | |
| dw, dh = 0.0, 0.0 | |
| new_unpad = (new_shape[1], new_shape[0]) | |
| ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios | |
| dw /= 2 # divide padding into 2 sides | |
| dh /= 2 | |
| if shape[::-1] != new_unpad: # resize | |
| img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_AREA) | |
| top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) | |
| left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) | |
| img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border | |
| return img, ratio, (dw, dh) | |
| def _box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n) | |
| # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio | |
| w1, h1 = box1[2] - box1[0], box1[3] - box1[1] | |
| w2, h2 = box2[2] - box2[0], box2[3] - box2[1] | |
| ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio | |
| return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates | |