172 lines
6.4 KiB
Python
172 lines
6.4 KiB
Python
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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# Transform back
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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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