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