Function random_sample_crop

scripts/augment_bbox.py:118–163

(height, width, boxes=None)

def random_sample_crop(height, width, boxes=None):
	global sample_options
	
	while True:
		# randomly choose a mode
		mode = random.choice(sample_options)
		if mode is None:
			return height, width, boxes

		min_iou, max_iou = mode
		if min_iou is None:
			min_iou = float('-inf')
		if max_iou is None:
			max_iou = float('inf')

		for _ in range(50):
			w = random.uniform(0.3 * width, width)
			h = random.uniform(0.3 * height, height)

			if h / w < 0.5 or h / w > 2:
				continue

			left = random.uniform(0, width - w)
			top = random.uniform(0, height - h)

			rect = np.array([int(left), int(top), int(left+w), int(top+h)])
			overlap = jaccard_numpy(boxes, rect)
			if overlap.min() < min_iou and max_iou < overlap.max():
				continue

			centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0

			m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
			m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
			mask = m1 * m2

			if not mask.any():
				continue

			current_boxes = boxes[mask, :].copy()
			current_boxes[:, :2] = np.maximum(current_boxes[:, :2], rect[:2])
			current_boxes[:, :2] -= rect[:2]
			current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:], rect[2:])
			current_boxes[:, 2:] -= rect[:2]

			return h, w, current_boxes


if __name__ == '__main__':