ykcheungab
129 lines
4.1 KiB
Python
129 lines
4.1 KiB
Python
import cv2
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import numpy as np
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def _prepare_draw_mask(valid_mask, shape):
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"""Resize a boolean mask to (height, width) for arrow culling."""
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if valid_mask is None:
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return None
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h, w = shape[:2]
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mask = np.asarray(valid_mask, dtype=bool)
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if mask.shape != (h, w):
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mask_u8 = mask.astype(np.uint8)
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if mask_u8.ndim > 2:
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mask_u8 = mask_u8[:, :, 0]
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mask = cv2.resize(mask_u8, (w, h), interpolation=cv2.INTER_NEAREST).astype(bool)
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return mask
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def draw_arrows(
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frame,
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optical_flow,
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fps=True,
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threshold=5,
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grid_spacing=40,
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arrow_scale=1.0,
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margin=0.8,
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colormap=cv2.COLORMAP_JET,
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below_threshold_color=(0, 0, 255),
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edge_margin=15,
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valid_mask=None,
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):
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"""
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Draw colored optical flow arrows on the image by magnitude (JET colormap).
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Frame and optical_flow must have the same height/width; arrows are drawn in pixel coordinates.
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Parameters
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----------
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valid_mask : array-like, optional
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Boolean (H, W) mask. Grid points outside the mask are skipped (no arrows or dots).
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"""
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image = frame.copy()
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if len(image.shape) == 2 or image.shape[2] == 1:
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
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h, w = image.shape[:2]
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mask = _prepare_draw_mask(valid_mask, (h, w))
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# Arrow grid in pixel coordinates (assume frame and flow same size)
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rr = np.arange(grid_spacing // 2, h, grid_spacing)
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cc = np.arange(grid_spacing // 2, w, grid_spacing)
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rr, cc = np.meshgrid(rr, cc, indexing="ij")
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rr, cc = rr.reshape(-1), cc.reshape(-1)
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# Displacement at each grid point (flow stored as half-displacement)
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disp = 2.0 * optical_flow[rr, cc, :]
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mag = np.linalg.norm(disp, axis=1)
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dx = disp[:, 0] / arrow_scale
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dy = disp[:, 1] / arrow_scale
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length = np.sqrt(dx * dx + dy * dy)
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# Cap arrow length at grid_spacing
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scale = np.where(length > grid_spacing * margin, grid_spacing / np.maximum(length, 1e-9), 1.0) * margin
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dx, dy = dx * scale, dy * scale
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end_x = (cc + dx).astype(np.int32)
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end_y = (rr + dy).astype(np.int32)
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valid = (
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(cc >= edge_margin)
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& (cc < w - edge_margin)
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& (rr >= edge_margin)
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& (rr < h - edge_margin)
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& (end_x >= 0)
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& (end_x < w)
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& (end_y >= 0)
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& (end_y < h)
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)
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if mask is not None:
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valid &= mask[rr, cc]
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# Arrow tips may fall outside the frame (the length cap is applied
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# before the bounds check above), so clamp before indexing: those points
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# are already False in ``valid``, and in-bounds indices are unchanged.
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valid &= mask[np.clip(end_y, 0, h - 1), np.clip(end_x, 0, w - 1)]
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mag_range = mag.max() - mag.min()
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norm_mag = (
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((mag - mag.min()) / mag_range * 255).astype(np.uint8) if mag_range > 0 else np.zeros_like(mag, dtype=np.uint8)
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)
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norm_mag[mag < threshold] = 0
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colors = cv2.applyColorMap(norm_mag.reshape(-1, 1), colormap)
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for i in np.where(valid)[0]:
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pt1, pt2 = (int(cc[i]), int(rr[i])), (int(end_x[i]), int(end_y[i]))
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color = tuple(int(c) for c in colors[i, 0])
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if mag[i] < threshold:
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cv2.circle(image, pt1, radius=1, color=below_threshold_color, thickness=-1)
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else:
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thickness = 1
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cv2.arrowedLine(image, pt1, pt2, color, thickness=thickness, tipLength=0.2)
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return image
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def draw_contact(image, contour, centroid, point_color=(0, 255, 0), region_color=(0, 255, 255)):
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"""
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在图像上绘制扩张焦点和扩张区域。
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Args:
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image: 背景图像
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contour: 轮廓
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centroid: 触碰点
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point_color: 触碰点的颜色 (BGR)
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region_color: 轮廓的颜色 (BGR)
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Returns:
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np.ndarray: 绘制了触碰点和轮廓的图像
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"""
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image = image.copy()
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# 绘制扩张区域轮廓
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if contour is not None:
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cv2.drawContours(image, [contour], -1, region_color, 2)
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# 绘制触碰点
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if centroid is not None:
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cx, cy = centroid
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cv2.circle(image, (cx, cy), 8, point_color, -1)
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cv2.circle(image, (cx, cy), 10, point_color, 2)
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return image
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