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import cv2 |
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import math |
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import numpy as np |
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from preprocess_ops import get_affine_transform |
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class HRNetPostProcess(object): |
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def __init__(self, use_dark=True): |
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self.use_dark = use_dark |
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def flip_back(self, output_flipped, matched_parts): |
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assert output_flipped.ndim == 4,\ |
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'output_flipped should be [batch_size, num_joints, height, width]' |
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output_flipped = output_flipped[:, :, :, ::-1] |
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for pair in matched_parts: |
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tmp = output_flipped[:, pair[0], :, :].copy() |
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output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] |
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output_flipped[:, pair[1], :, :] = tmp |
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return output_flipped |
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def get_max_preds(self, heatmaps): |
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"""get predictions from score maps |
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Args: |
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heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) |
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Returns: |
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preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords |
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maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the keypoints |
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""" |
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assert isinstance(heatmaps, |
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np.ndarray), 'heatmaps should be numpy.ndarray' |
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assert heatmaps.ndim == 4, 'batch_images should be 4-ndim' |
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batch_size = heatmaps.shape[0] |
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num_joints = heatmaps.shape[1] |
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width = heatmaps.shape[3] |
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heatmaps_reshaped = heatmaps.reshape((batch_size, num_joints, -1)) |
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idx = np.argmax(heatmaps_reshaped, 2) |
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maxvals = np.amax(heatmaps_reshaped, 2) |
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maxvals = maxvals.reshape((batch_size, num_joints, 1)) |
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idx = idx.reshape((batch_size, num_joints, 1)) |
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preds = np.tile(idx, (1, 1, 2)).astype(np.float32) |
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preds[:, :, 0] = (preds[:, :, 0]) % width |
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preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) |
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pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) |
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pred_mask = pred_mask.astype(np.float32) |
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preds *= pred_mask |
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return preds, maxvals |
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def gaussian_blur(self, heatmap, kernel): |
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border = (kernel - 1) // 2 |
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batch_size = heatmap.shape[0] |
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num_joints = heatmap.shape[1] |
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height = heatmap.shape[2] |
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width = heatmap.shape[3] |
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for i in range(batch_size): |
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for j in range(num_joints): |
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origin_max = np.max(heatmap[i, j]) |
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dr = np.zeros((height + 2 * border, width + 2 * border)) |
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dr[border:-border, border:-border] = heatmap[i, j].copy() |
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dr = cv2.GaussianBlur(dr, (kernel, kernel), 0) |
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heatmap[i, j] = dr[border:-border, border:-border].copy() |
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heatmap[i, j] *= origin_max / np.max(heatmap[i, j]) |
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return heatmap |
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def dark_parse(self, hm, coord): |
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heatmap_height = hm.shape[0] |
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heatmap_width = hm.shape[1] |
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px = int(coord[0]) |
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py = int(coord[1]) |
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if 1 < px < heatmap_width - 2 and 1 < py < heatmap_height - 2: |
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dx = 0.5 * (hm[py][px + 1] - hm[py][px - 1]) |
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dy = 0.5 * (hm[py + 1][px] - hm[py - 1][px]) |
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dxx = 0.25 * (hm[py][px + 2] - 2 * hm[py][px] + hm[py][px - 2]) |
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dxy = 0.25 * (hm[py+1][px+1] - hm[py-1][px+1] - hm[py+1][px-1] \ |
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+ hm[py-1][px-1]) |
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dyy = 0.25 * ( |
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hm[py + 2 * 1][px] - 2 * hm[py][px] + hm[py - 2 * 1][px]) |
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derivative = np.matrix([[dx], [dy]]) |
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hessian = np.matrix([[dxx, dxy], [dxy, dyy]]) |
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if dxx * dyy - dxy**2 != 0: |
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hessianinv = hessian.I |
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offset = -hessianinv * derivative |
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offset = np.squeeze(np.array(offset.T), axis=0) |
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coord += offset |
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return coord |
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def dark_postprocess(self, hm, coords, kernelsize): |
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""" |
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refer to https://github.com/ilovepose/DarkPose/lib/core/inference.py |
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""" |
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hm = self.gaussian_blur(hm, kernelsize) |
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hm = np.maximum(hm, 1e-10) |
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hm = np.log(hm) |
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for n in range(coords.shape[0]): |
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for p in range(coords.shape[1]): |
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coords[n, p] = self.dark_parse(hm[n][p], coords[n][p]) |
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return coords |
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def get_final_preds(self, heatmaps, center, scale, kernelsize=3): |
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"""the highest heatvalue location with a quarter offset in the |
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direction from the highest response to the second highest response. |
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Args: |
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heatmaps (numpy.ndarray): The predicted heatmaps |
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center (numpy.ndarray): The boxes center |
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scale (numpy.ndarray): The scale factor |
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Returns: |
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preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords |
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maxvals: numpy.ndarray([batch_size, num_joints, 1]), the maximum confidence of the keypoints |
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""" |
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coords, maxvals = self.get_max_preds(heatmaps) |
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heatmap_height = heatmaps.shape[2] |
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heatmap_width = heatmaps.shape[3] |
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if self.use_dark: |
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coords = self.dark_postprocess(heatmaps, coords, kernelsize) |
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else: |
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for n in range(coords.shape[0]): |
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for p in range(coords.shape[1]): |
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hm = heatmaps[n][p] |
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px = int(math.floor(coords[n][p][0] + 0.5)) |
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py = int(math.floor(coords[n][p][1] + 0.5)) |
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if 1 < px < heatmap_width - 1 and 1 < py < heatmap_height - 1: |
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diff = np.array([ |
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hm[py][px + 1] - hm[py][px - 1], |
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hm[py + 1][px] - hm[py - 1][px] |
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]) |
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coords[n][p] += np.sign(diff) * .25 |
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preds = coords.copy() |
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for i in range(coords.shape[0]): |
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preds[i] = transform_preds(coords[i], center[i], scale[i], |
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[heatmap_width, heatmap_height]) |
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return preds, maxvals |
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def __call__(self, output, center, scale): |
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preds, maxvals = self.get_final_preds(output, center, scale) |
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return np.concatenate( |
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(preds, maxvals), axis=-1), np.mean( |
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maxvals, axis=1) |
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def transform_preds(coords, center, scale, output_size): |
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target_coords = np.zeros(coords.shape) |
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trans = get_affine_transform(center, scale * 200, 0, output_size, inv=1) |
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for p in range(coords.shape[0]): |
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target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) |
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return target_coords |
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def affine_transform(pt, t): |
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new_pt = np.array([pt[0], pt[1], 1.]).T |
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new_pt = np.dot(t, new_pt) |
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return new_pt[:2] |
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