import cv2 import numpy as np import torch from omegaconf import OmegaConf from ultralytics.yolo.data import build_dataloader class Colors: # Ultralytics color palette https://ultralytics.com/ def __init__(self): # hex = matplotlib.colors.TABLEAU_COLORS.values() hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') self.palette = [self.hex2rgb(f'#{c}') for c in hexs] self.n = len(self.palette) def __call__(self, i, bgr=False): c = self.palette[int(i) % self.n] return (c[2], c[1], c[0]) if bgr else c @staticmethod def hex2rgb(h): # rgb order (PIL) return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) colors = Colors() # create instance for 'from utils.plots import colors' def plot_one_box(x, img, keypoints=None, color=None, label=None, line_thickness=None): import random # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText( img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA, ) if keypoints is not None: plot_keypoint(img, keypoints, color, tl) def plot_keypoint(img, keypoints, color, tl): num_l = len(keypoints) # clors = [(255, 0, 0),(0, 255, 0),(0, 0, 255),(255, 255, 0),(0, 255, 255)] # clors = [[random.randint(0, 255) for _ in range(3)] for _ in range(num_l)] for i in range(num_l): point_x = int(keypoints[i][0]) point_y = int(keypoints[i][1]) cv2.circle(img, (point_x, point_y), tl + 3, color, -1) with open("ultralytics/tests/data/dataloader/hyp_test.yaml") as f: hyp = OmegaConf.load(f) def test(augment, rect): dataloader, _ = build_dataloader( img_path="/d/dataset/COCO/images/val2017", img_size=640, label_path=None, cache=False, hyp=hyp, augment=augment, prefix="", rect=rect, batch_size=4, stride=32, pad=0.5, use_segments=False, use_keypoints=True, ) for d in dataloader: idx = 1 # show which image inside one batch img = d["img"][idx].numpy() img = np.ascontiguousarray(img.transpose(1, 2, 0)) ih, iw = img.shape[:2] # print(img.shape) bidx = d["batch_idx"] cls = d["cls"][bidx == idx].numpy() bboxes = d["bboxes"][bidx == idx].numpy() bboxes[:, [0, 2]] *= iw bboxes[:, [1, 3]] *= ih keypoints = d["keypoints"][bidx == idx] keypoints[..., 0] *= iw keypoints[..., 1] *= ih # print(keypoints, keypoints.shape) # print(d["im_file"]) for i, b in enumerate(bboxes): x, y, w, h = b x1 = x - w / 2 x2 = x + w / 2 y1 = y - h / 2 y2 = y + h / 2 c = int(cls[i][0]) # print(x1, y1, x2, y2) plot_one_box([int(x1), int(y1), int(x2), int(y2)], img, keypoints=keypoints[i], label=f"{c}", color=colors(c)) cv2.imshow("p", img) if cv2.waitKey(0) == ord("q"): break if __name__ == "__main__": test(augment=True, rect=False) test(augment=False, rect=True) test(augment=False, rect=False)