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import cv2 |
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import numpy as np |
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from scipy import interpolate |
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def intersection(x, p1, p2): |
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x1, y1 = p1 |
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x2, y2 = p2 |
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if x2 == x1: |
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return 0 |
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k = (x - x1) / (x2 - x1) |
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return k * (y2 - y1) + y1 |
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def midpoint(p1, p2, typed=float): |
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return [typed((p1[0] + p2[0]) / 2), typed((p1[1] + p2[1]) / 2)] |
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def resize_with_coordinates(image, width, height, coordinates): |
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original_height, original_width = image.shape[:2] |
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resized_image = cv2.resize(image, (width, height)) |
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if coordinates is not None: |
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assert coordinates.ndim == 2 |
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assert coordinates.shape[-1] == 2 |
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rate_x = width / original_width |
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rate_y = height / original_height |
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coordinates = coordinates * (rate_x, rate_y) |
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return resized_image, coordinates |
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def box2seg(image, boxes, label): |
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height, width = image.shape[:2] |
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mask = np.zeros((height, width), dtype=np.float32) |
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seg = np.zeros((height, width), dtype=np.float32) |
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points = [] |
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for box_index in range(boxes.shape[0]): |
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box = boxes[box_index, :, :] |
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left_top = box[0] |
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right_top = box[1] |
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right_bottom = box[2] |
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left_bottom = box[3] |
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left = [(left_top[0] + left_bottom[0]) / 2, (left_top[1] + left_bottom[1]) / 2] |
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right = [(right_top[0] + right_bottom[0]) / 2, (right_top[1] + right_bottom[1]) / 2] |
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center = midpoint(left, right) |
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points.append(midpoint(left, center)) |
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points.append(midpoint(right, center)) |
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poly = np.array([midpoint(left_top, center), |
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midpoint(right_top, center), |
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midpoint(right_bottom, center), |
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midpoint(left_bottom, center) |
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]) |
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seg = cv2.fillPoly(seg, [poly.reshape(4, 1, 2).astype(np.int32)], int(label[box_index])) |
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left_y = intersection(0, points[0], points[1]) |
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right_y = intersection(width, points[-1], points[-2]) |
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points.insert(0, [0, left_y]) |
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points.append([width, right_y]) |
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points = np.array(points) |
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f = interpolate.interp1d(points[:, 0], points[:, 1], fill_value='extrapolate') |
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xnew = np.arange(0, width, 1) |
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ynew = f(xnew).clip(0, height-1) |
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for x in range(width - 1): |
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mask[int(ynew[x]), x] = 1 |
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return ynew.reshape(1, -1).round(), seg |
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