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837 lines
29 KiB
Python
837 lines
29 KiB
Python
"""Functions for human pose estimation"""
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import math
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from collections import OrderedDict
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from functools import lru_cache
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import cv2
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import matplotlib as mpl
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import numpy as np
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import torch
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from scipy.ndimage.filters import gaussian_filter
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from torch import nn
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from imaginairy.img_utils import torch_image_to_openvcv_img
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from imaginairy.model_manager import get_cached_url_path
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from imaginairy.utils import get_device
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def pad_right_down_corner(img, stride, padValue):
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h = img.shape[0]
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w = img.shape[1]
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pad = 4 * [None]
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pad[0] = 0 # up
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pad[1] = 0 # left
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pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
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pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
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img_padded = img
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pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
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img_padded = np.concatenate((pad_up, img_padded), axis=0)
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pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
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img_padded = np.concatenate((pad_left, img_padded), axis=1)
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pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
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img_padded = np.concatenate((img_padded, pad_down), axis=0)
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pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
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img_padded = np.concatenate((img_padded, pad_right), axis=1)
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return img_padded, pad
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def transfer(model, model_weights):
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# transfer caffe model to pytorch which will match the layer name
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transfered_model_weights = {}
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for weights_name in model.state_dict():
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transfered_model_weights[weights_name] = model_weights[
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".".join(weights_name.split(".")[1:])
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]
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return transfered_model_weights
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# draw the body keypoint and lims
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def draw_bodypose(canvas, candidate, subset):
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stickwidth = 4
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limbSeq = [
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[2, 3],
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[2, 6],
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[3, 4],
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[4, 5],
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[6, 7],
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[7, 8],
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[2, 9],
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[9, 10],
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[10, 11],
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[2, 12],
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[12, 13],
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[13, 14],
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[2, 1],
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[1, 15],
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[15, 17],
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[1, 16],
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[16, 18],
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[3, 17],
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[6, 18],
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]
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colors = [
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[255, 0, 0],
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[255, 85, 0],
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[255, 170, 0],
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[255, 255, 0],
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[170, 255, 0],
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[85, 255, 0],
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[0, 255, 0],
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[0, 255, 85],
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[0, 255, 170],
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[0, 255, 255],
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[0, 170, 255],
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[0, 85, 255],
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[0, 0, 255],
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[85, 0, 255],
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[170, 0, 255],
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[255, 0, 255],
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[255, 0, 170],
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[255, 0, 85],
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]
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for i in range(18):
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for n in range(len(subset)):
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index = int(subset[n][i])
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if index == -1:
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continue
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x, y = candidate[index][0:2]
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cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
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for i in range(17):
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for n in range(len(subset)):
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index = subset[n][np.array(limbSeq[i]) - 1]
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if -1 in index:
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continue
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cur_canvas = canvas.copy()
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Y = candidate[index.astype(int), 0]
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X = candidate[index.astype(int), 1]
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mX = np.mean(X)
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mY = np.mean(Y)
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length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
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polygon = cv2.ellipse2Poly(
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(int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1
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)
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cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
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canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
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# plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])
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# plt.imshow(canvas[:, :, [2, 1, 0]])
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return canvas
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# image drawed by opencv is not good.
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def draw_handpose(canvas, all_hand_peaks, show_number=False):
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edges = [
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[0, 1],
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[1, 2],
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[2, 3],
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[3, 4],
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[0, 5],
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[5, 6],
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[6, 7],
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[7, 8],
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[0, 9],
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[9, 10],
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[10, 11],
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[11, 12],
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[0, 13],
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[13, 14],
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[14, 15],
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[15, 16],
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[0, 17],
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[17, 18],
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[18, 19],
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[19, 20],
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]
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for peaks in all_hand_peaks:
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for ie, e in enumerate(edges):
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if np.sum(np.all(peaks[e], axis=1) == 0) == 0:
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x1, y1 = peaks[e[0]]
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x2, y2 = peaks[e[1]]
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cv2.line(
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canvas,
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(x1, y1),
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(x2, y2),
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mpl.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
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thickness=2,
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)
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for i, keyponit in enumerate(peaks):
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x, y = keyponit
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cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
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if show_number:
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cv2.putText(
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canvas,
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str(i),
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(x, y),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.3,
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(0, 0, 0),
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lineType=cv2.LINE_AA,
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)
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return canvas
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# detect hand according to body pose keypoints
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# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
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def handDetect(candidate, subset, oriImg):
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# right hand: wrist 4, elbow 3, shoulder 2
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# left hand: wrist 7, elbow 6, shoulder 5
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ratioWristElbow = 0.33
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detect_result = []
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image_height, image_width = oriImg.shape[0:2]
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for person in subset.astype(int):
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# if any of three not detected
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has_left = np.sum(person[[5, 6, 7]] == -1) == 0
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has_right = np.sum(person[[2, 3, 4]] == -1) == 0
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if not (has_left or has_right):
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continue
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hands = []
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# left hand
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if has_left:
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left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
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x1, y1 = candidate[left_shoulder_index][:2]
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x2, y2 = candidate[left_elbow_index][:2]
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x3, y3 = candidate[left_wrist_index][:2]
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hands.append([x1, y1, x2, y2, x3, y3, True])
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# right hand
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if has_right:
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right_shoulder_index, right_elbow_index, right_wrist_index = person[
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[2, 3, 4]
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]
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x1, y1 = candidate[right_shoulder_index][:2]
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x2, y2 = candidate[right_elbow_index][:2]
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x3, y3 = candidate[right_wrist_index][:2]
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hands.append([x1, y1, x2, y2, x3, y3, False])
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for x1, y1, x2, y2, x3, y3, is_left in hands:
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# pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
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# handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
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# handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
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# const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
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# const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
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# handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
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x = x3 + ratioWristElbow * (x3 - x2)
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y = y3 + ratioWristElbow * (y3 - y2)
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distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
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distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
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# x-y refers to the center --> offset to topLeft point
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# handRectangle.x -= handRectangle.width / 2.f;
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# handRectangle.y -= handRectangle.height / 2.f;
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x -= width / 2
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y -= width / 2 # width = height
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# overflow the image
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x = max(x, 0)
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y = max(y, 0)
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width1 = width
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width2 = width
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if x + width > image_width:
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width1 = image_width - x
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if y + width > image_height:
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width2 = image_height - y
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width = min(width1, width2)
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# the max hand box value is 20 pixels
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if width >= 20:
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detect_result.append([int(x), int(y), int(width), is_left])
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# return value: [[x, y, w, True if left hand else False]].
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# width=height since the network require squared input.
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# x, y is the coordinate of top left
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return detect_result
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# get max index of 2d array
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def npmax(array):
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arrayindex = array.argmax(1)
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arrayvalue = array.max(1)
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i = arrayvalue.argmax()
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j = arrayindex[i]
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return i, j
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def make_layers(block, no_relu_layers):
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layers = []
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for layer_name, v in block.items():
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if "pool" in layer_name:
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layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], padding=v[2])
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layers.append((layer_name, layer))
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else:
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conv2d = nn.Conv2d(
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in_channels=v[0],
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out_channels=v[1],
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kernel_size=v[2],
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stride=v[3],
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padding=v[4],
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)
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layers.append((layer_name, conv2d))
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if layer_name not in no_relu_layers:
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layers.append(("relu_" + layer_name, nn.ReLU(inplace=True)))
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return nn.Sequential(OrderedDict(layers))
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class bodypose_model(nn.Module):
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def __init__(self):
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super().__init__()
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# these layers have no relu layer
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no_relu_layers = [
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"conv5_5_CPM_L1",
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"conv5_5_CPM_L2",
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"Mconv7_stage2_L1",
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"Mconv7_stage2_L2",
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"Mconv7_stage3_L1",
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"Mconv7_stage3_L2",
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"Mconv7_stage4_L1",
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"Mconv7_stage4_L2",
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"Mconv7_stage5_L1",
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"Mconv7_stage5_L2",
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"Mconv7_stage6_L1",
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"Mconv7_stage6_L1",
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]
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blocks = {}
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block0 = OrderedDict(
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[
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("conv1_1", [3, 64, 3, 1, 1]),
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("conv1_2", [64, 64, 3, 1, 1]),
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("pool1_stage1", [2, 2, 0]),
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("conv2_1", [64, 128, 3, 1, 1]),
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("conv2_2", [128, 128, 3, 1, 1]),
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("pool2_stage1", [2, 2, 0]),
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("conv3_1", [128, 256, 3, 1, 1]),
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("conv3_2", [256, 256, 3, 1, 1]),
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("conv3_3", [256, 256, 3, 1, 1]),
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("conv3_4", [256, 256, 3, 1, 1]),
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("pool3_stage1", [2, 2, 0]),
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("conv4_1", [256, 512, 3, 1, 1]),
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("conv4_2", [512, 512, 3, 1, 1]),
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("conv4_3_CPM", [512, 256, 3, 1, 1]),
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("conv4_4_CPM", [256, 128, 3, 1, 1]),
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]
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)
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# Stage 1
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block1_1 = OrderedDict(
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[
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("conv5_1_CPM_L1", [128, 128, 3, 1, 1]),
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("conv5_2_CPM_L1", [128, 128, 3, 1, 1]),
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("conv5_3_CPM_L1", [128, 128, 3, 1, 1]),
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("conv5_4_CPM_L1", [128, 512, 1, 1, 0]),
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("conv5_5_CPM_L1", [512, 38, 1, 1, 0]),
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]
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)
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block1_2 = OrderedDict(
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[
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("conv5_1_CPM_L2", [128, 128, 3, 1, 1]),
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("conv5_2_CPM_L2", [128, 128, 3, 1, 1]),
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("conv5_3_CPM_L2", [128, 128, 3, 1, 1]),
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("conv5_4_CPM_L2", [128, 512, 1, 1, 0]),
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("conv5_5_CPM_L2", [512, 19, 1, 1, 0]),
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]
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)
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blocks["block1_1"] = block1_1
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blocks["block1_2"] = block1_2
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self.model0 = make_layers(block0, no_relu_layers)
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# Stages 2 - 6
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for i in range(2, 7):
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blocks[f"block{i}_1"] = OrderedDict(
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[
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(f"Mconv1_stage{i}_L1", [185, 128, 7, 1, 3]),
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(f"Mconv2_stage{i}_L1", [128, 128, 7, 1, 3]),
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(f"Mconv3_stage{i}_L1", [128, 128, 7, 1, 3]),
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(f"Mconv4_stage{i}_L1", [128, 128, 7, 1, 3]),
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(f"Mconv5_stage{i}_L1", [128, 128, 7, 1, 3]),
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(f"Mconv6_stage{i}_L1", [128, 128, 1, 1, 0]),
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(f"Mconv7_stage{i}_L1", [128, 38, 1, 1, 0]),
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]
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)
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blocks[f"block{i}_2"] = OrderedDict(
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[
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(f"Mconv1_stage{i}_L2", [185, 128, 7, 1, 3]),
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(f"Mconv2_stage{i}_L2", [128, 128, 7, 1, 3]),
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(f"Mconv3_stage{i}_L2", [128, 128, 7, 1, 3]),
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(f"Mconv4_stage{i}_L2", [128, 128, 7, 1, 3]),
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(f"Mconv5_stage{i}_L2", [128, 128, 7, 1, 3]),
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(f"Mconv6_stage{i}_L2", [128, 128, 1, 1, 0]),
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(f"Mconv7_stage{i}_L2", [128, 19, 1, 1, 0]),
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]
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)
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for k in blocks:
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blocks[k] = make_layers(blocks[k], no_relu_layers)
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self.model1_1 = blocks["block1_1"]
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self.model2_1 = blocks["block2_1"]
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self.model3_1 = blocks["block3_1"]
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self.model4_1 = blocks["block4_1"]
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self.model5_1 = blocks["block5_1"]
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self.model6_1 = blocks["block6_1"]
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self.model1_2 = blocks["block1_2"]
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self.model2_2 = blocks["block2_2"]
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self.model3_2 = blocks["block3_2"]
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self.model4_2 = blocks["block4_2"]
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self.model5_2 = blocks["block5_2"]
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self.model6_2 = blocks["block6_2"]
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def forward(self, x):
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out1 = self.model0(x)
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out1_1 = self.model1_1(out1)
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out1_2 = self.model1_2(out1)
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out2 = torch.cat([out1_1, out1_2, out1], 1)
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out2_1 = self.model2_1(out2)
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out2_2 = self.model2_2(out2)
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out3 = torch.cat([out2_1, out2_2, out1], 1)
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out3_1 = self.model3_1(out3)
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out3_2 = self.model3_2(out3)
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out4 = torch.cat([out3_1, out3_2, out1], 1)
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out4_1 = self.model4_1(out4)
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out4_2 = self.model4_2(out4)
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out5 = torch.cat([out4_1, out4_2, out1], 1)
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out5_1 = self.model5_1(out5)
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out5_2 = self.model5_2(out5)
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out6 = torch.cat([out5_1, out5_2, out1], 1)
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out6_1 = self.model6_1(out6)
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out6_2 = self.model6_2(out6)
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return out6_1, out6_2
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class handpose_model(nn.Module):
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def __init__(self):
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super().__init__()
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# these layers have no relu layer
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no_relu_layers = [
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"conv6_2_CPM",
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"Mconv7_stage2",
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"Mconv7_stage3",
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"Mconv7_stage4",
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"Mconv7_stage5",
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"Mconv7_stage6",
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]
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# stage 1
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block1_0 = OrderedDict(
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[
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("conv1_1", [3, 64, 3, 1, 1]),
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("conv1_2", [64, 64, 3, 1, 1]),
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("pool1_stage1", [2, 2, 0]),
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("conv2_1", [64, 128, 3, 1, 1]),
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("conv2_2", [128, 128, 3, 1, 1]),
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("pool2_stage1", [2, 2, 0]),
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("conv3_1", [128, 256, 3, 1, 1]),
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("conv3_2", [256, 256, 3, 1, 1]),
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("conv3_3", [256, 256, 3, 1, 1]),
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("conv3_4", [256, 256, 3, 1, 1]),
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("pool3_stage1", [2, 2, 0]),
|
|
("conv4_1", [256, 512, 3, 1, 1]),
|
|
("conv4_2", [512, 512, 3, 1, 1]),
|
|
("conv4_3", [512, 512, 3, 1, 1]),
|
|
("conv4_4", [512, 512, 3, 1, 1]),
|
|
("conv5_1", [512, 512, 3, 1, 1]),
|
|
("conv5_2", [512, 512, 3, 1, 1]),
|
|
("conv5_3_CPM", [512, 128, 3, 1, 1]),
|
|
]
|
|
)
|
|
|
|
block1_1 = OrderedDict(
|
|
[("conv6_1_CPM", [128, 512, 1, 1, 0]), ("conv6_2_CPM", [512, 22, 1, 1, 0])]
|
|
)
|
|
|
|
blocks = {}
|
|
blocks["block1_0"] = block1_0
|
|
blocks["block1_1"] = block1_1
|
|
|
|
# stage 2-6
|
|
for i in range(2, 7):
|
|
blocks[f"block{i}"] = OrderedDict(
|
|
[
|
|
(f"Mconv1_stage{i}", [150, 128, 7, 1, 3]),
|
|
(f"Mconv2_stage{i}", [128, 128, 7, 1, 3]),
|
|
(f"Mconv3_stage{i}", [128, 128, 7, 1, 3]),
|
|
(f"Mconv4_stage{i}", [128, 128, 7, 1, 3]),
|
|
(f"Mconv5_stage{i}", [128, 128, 7, 1, 3]),
|
|
(f"Mconv6_stage{i}", [128, 128, 1, 1, 0]),
|
|
(f"Mconv7_stage{i}", [128, 22, 1, 1, 0]),
|
|
]
|
|
)
|
|
|
|
for k in blocks:
|
|
blocks[k] = make_layers(blocks[k], no_relu_layers)
|
|
|
|
self.model1_0 = blocks["block1_0"]
|
|
self.model1_1 = blocks["block1_1"]
|
|
self.model2 = blocks["block2"]
|
|
self.model3 = blocks["block3"]
|
|
self.model4 = blocks["block4"]
|
|
self.model5 = blocks["block5"]
|
|
self.model6 = blocks["block6"]
|
|
|
|
def forward(self, x):
|
|
out1_0 = self.model1_0(x)
|
|
out1_1 = self.model1_1(out1_0)
|
|
concat_stage2 = torch.cat([out1_1, out1_0], 1)
|
|
out_stage2 = self.model2(concat_stage2)
|
|
concat_stage3 = torch.cat([out_stage2, out1_0], 1)
|
|
out_stage3 = self.model3(concat_stage3)
|
|
concat_stage4 = torch.cat([out_stage3, out1_0], 1)
|
|
out_stage4 = self.model4(concat_stage4)
|
|
concat_stage5 = torch.cat([out_stage4, out1_0], 1)
|
|
out_stage5 = self.model5(concat_stage5)
|
|
concat_stage6 = torch.cat([out_stage5, out1_0], 1)
|
|
out_stage6 = self.model6(concat_stage6)
|
|
return out_stage6
|
|
|
|
|
|
@lru_cache(maxsize=1)
|
|
def openpose_model():
|
|
model = bodypose_model()
|
|
weights_url = "https://huggingface.co/lllyasviel/ControlNet/resolve/38a62cbf79862c1bac73405ec8dc46133aee3e36/annotator/ckpts/body_pose_model.pth"
|
|
model_path = get_cached_url_path(weights_url)
|
|
model_dict = transfer(model, torch.load(model_path))
|
|
model.load_state_dict(model_dict)
|
|
model.eval()
|
|
return model
|
|
|
|
|
|
def create_body_pose_img(original_img_t):
|
|
candidate, subset = create_body_pose(original_img_t)
|
|
canvas = np.zeros((original_img_t.shape[2], original_img_t.shape[3], 3))
|
|
canvas = draw_bodypose(canvas, candidate, subset)
|
|
canvas = torch.from_numpy(canvas).to(dtype=torch.float32)
|
|
# canvas = canvas.unsqueeze(0)
|
|
canvas = canvas.permute(2, 0, 1).unsqueeze(0)
|
|
return canvas
|
|
|
|
|
|
def create_body_pose(original_img_t):
|
|
original_img = torch_image_to_openvcv_img(original_img_t)
|
|
|
|
model = openpose_model()
|
|
# scale_search = [0.5, 1.0, 1.5, 2.0]
|
|
scale_search = [0.5]
|
|
boxsize = 368
|
|
stride = 8
|
|
padValue = 128
|
|
thre1 = 0.1
|
|
thre2 = 0.05
|
|
multiplier = [x * boxsize / original_img.shape[0] for x in scale_search]
|
|
heatmap_avg = np.zeros((original_img.shape[0], original_img.shape[1], 19))
|
|
paf_avg = np.zeros((original_img.shape[0], original_img.shape[1], 38))
|
|
|
|
for m, scale in enumerate(multiplier):
|
|
imageToTest = cv2.resize(
|
|
original_img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
|
|
)
|
|
imageToTest_padded, pad = pad_right_down_corner(imageToTest, stride, padValue)
|
|
im = (
|
|
np.transpose(
|
|
np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)
|
|
)
|
|
/ 256
|
|
- 0.5
|
|
)
|
|
im = np.ascontiguousarray(im)
|
|
|
|
data = torch.from_numpy(im).float()
|
|
data.to(get_device())
|
|
|
|
# data = data.permute([2, 0, 1]).unsqueeze(0).float()
|
|
with torch.no_grad():
|
|
Mconv7_stage6_L1, Mconv7_stage6_L2 = model(data)
|
|
Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
|
|
Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
|
|
|
|
# extract outputs, resize, and remove padding
|
|
# heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps
|
|
heatmap = np.transpose(
|
|
np.squeeze(Mconv7_stage6_L2), (1, 2, 0)
|
|
) # output 1 is heatmaps
|
|
heatmap = cv2.resize(
|
|
heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC
|
|
)
|
|
heatmap = heatmap[
|
|
: imageToTest_padded.shape[0] - pad[2],
|
|
: imageToTest_padded.shape[1] - pad[3],
|
|
:,
|
|
]
|
|
heatmap = cv2.resize(
|
|
heatmap,
|
|
(original_img.shape[1], original_img.shape[0]),
|
|
interpolation=cv2.INTER_CUBIC,
|
|
)
|
|
|
|
# paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs
|
|
paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs
|
|
paf = cv2.resize(
|
|
paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC
|
|
)
|
|
paf = paf[
|
|
: imageToTest_padded.shape[0] - pad[2],
|
|
: imageToTest_padded.shape[1] - pad[3],
|
|
:,
|
|
]
|
|
paf = cv2.resize(
|
|
paf,
|
|
(original_img.shape[1], original_img.shape[0]),
|
|
interpolation=cv2.INTER_CUBIC,
|
|
)
|
|
|
|
heatmap_avg += heatmap_avg + heatmap / len(multiplier)
|
|
paf_avg += +paf / len(multiplier)
|
|
|
|
all_peaks = []
|
|
peak_counter = 0
|
|
|
|
for part in range(18):
|
|
map_ori = heatmap_avg[:, :, part]
|
|
one_heatmap = gaussian_filter(map_ori, sigma=3)
|
|
|
|
map_left = np.zeros(one_heatmap.shape)
|
|
map_left[1:, :] = one_heatmap[:-1, :]
|
|
map_right = np.zeros(one_heatmap.shape)
|
|
map_right[:-1, :] = one_heatmap[1:, :]
|
|
map_up = np.zeros(one_heatmap.shape)
|
|
map_up[:, 1:] = one_heatmap[:, :-1]
|
|
map_down = np.zeros(one_heatmap.shape)
|
|
map_down[:, :-1] = one_heatmap[:, 1:]
|
|
|
|
peaks_binary = np.logical_and.reduce(
|
|
(
|
|
one_heatmap >= map_left,
|
|
one_heatmap >= map_right,
|
|
one_heatmap >= map_up,
|
|
one_heatmap >= map_down,
|
|
one_heatmap > thre1,
|
|
)
|
|
)
|
|
peaks = list(
|
|
zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])
|
|
) # note reverse
|
|
peaks_with_score = [(*x, map_ori[x[1], x[0]]) for x in peaks]
|
|
peak_id = range(peak_counter, peak_counter + len(peaks))
|
|
peaks_with_score_and_id = [
|
|
peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))
|
|
]
|
|
|
|
all_peaks.append(peaks_with_score_and_id)
|
|
peak_counter += len(peaks)
|
|
|
|
# find connection in the specified sequence, center 29 is in the position 15
|
|
limbSeq = [
|
|
[2, 3],
|
|
[2, 6],
|
|
[3, 4],
|
|
[4, 5],
|
|
[6, 7],
|
|
[7, 8],
|
|
[2, 9],
|
|
[9, 10],
|
|
[10, 11],
|
|
[2, 12],
|
|
[12, 13],
|
|
[13, 14],
|
|
[2, 1],
|
|
[1, 15],
|
|
[15, 17],
|
|
[1, 16],
|
|
[16, 18],
|
|
[3, 17],
|
|
[6, 18],
|
|
]
|
|
# the middle joints heatmap correpondence
|
|
mapIdx = [
|
|
[31, 32],
|
|
[39, 40],
|
|
[33, 34],
|
|
[35, 36],
|
|
[41, 42],
|
|
[43, 44],
|
|
[19, 20],
|
|
[21, 22],
|
|
[23, 24],
|
|
[25, 26],
|
|
[27, 28],
|
|
[29, 30],
|
|
[47, 48],
|
|
[49, 50],
|
|
[53, 54],
|
|
[51, 52],
|
|
[55, 56],
|
|
[37, 38],
|
|
[45, 46],
|
|
]
|
|
|
|
connection_all = []
|
|
special_k = []
|
|
mid_num = 10
|
|
|
|
for k in range(len(mapIdx)):
|
|
score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
|
|
candA = all_peaks[limbSeq[k][0] - 1]
|
|
candB = all_peaks[limbSeq[k][1] - 1]
|
|
nA = len(candA)
|
|
nB = len(candB)
|
|
indexA, indexB = limbSeq[k]
|
|
if nA != 0 and nB != 0:
|
|
connection_candidate = []
|
|
for i in range(nA):
|
|
for j in range(nB):
|
|
vec = np.subtract(candB[j][:2], candA[i][:2])
|
|
norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
|
|
norm = max(0.001, norm)
|
|
vec = np.divide(vec, norm)
|
|
|
|
startend = list(
|
|
zip(
|
|
np.linspace(candA[i][0], candB[j][0], num=mid_num),
|
|
np.linspace(candA[i][1], candB[j][1], num=mid_num),
|
|
)
|
|
)
|
|
|
|
vec_x = np.array(
|
|
[
|
|
score_mid[
|
|
int(round(startend[I][1])),
|
|
int(round(startend[I][0])),
|
|
0,
|
|
]
|
|
for I in range(len(startend)) # noqa
|
|
]
|
|
)
|
|
vec_y = np.array(
|
|
[
|
|
score_mid[
|
|
int(round(startend[I][1])),
|
|
int(round(startend[I][0])),
|
|
1,
|
|
]
|
|
for I in range(len(startend)) # noqa
|
|
]
|
|
)
|
|
|
|
score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(
|
|
vec_y, vec[1]
|
|
)
|
|
score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
|
|
0.5 * original_img.shape[0] / norm - 1, 0
|
|
)
|
|
criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(
|
|
score_midpts
|
|
)
|
|
criterion2 = score_with_dist_prior > 0
|
|
if criterion1 and criterion2:
|
|
connection_candidate.append(
|
|
[
|
|
i,
|
|
j,
|
|
score_with_dist_prior,
|
|
score_with_dist_prior + candA[i][2] + candB[j][2],
|
|
]
|
|
)
|
|
|
|
connection_candidate = sorted(
|
|
connection_candidate, key=lambda x: x[2], reverse=True
|
|
)
|
|
connection = np.zeros((0, 5))
|
|
for c in range(len(connection_candidate)):
|
|
i, j, s = connection_candidate[c][0:3]
|
|
if i not in connection[:, 3] and j not in connection[:, 4]:
|
|
connection = np.vstack(
|
|
[connection, [candA[i][3], candB[j][3], s, i, j]]
|
|
)
|
|
if len(connection) >= min(nA, nB):
|
|
break
|
|
|
|
connection_all.append(connection)
|
|
else:
|
|
special_k.append(k)
|
|
connection_all.append([])
|
|
|
|
# last number in each row is the total parts number of that person
|
|
# the second last number in each row is the score of the overall configuration
|
|
subset = -1 * np.ones((0, 20))
|
|
candidate = np.array([item for sublist in all_peaks for item in sublist])
|
|
|
|
for k in range(len(mapIdx)):
|
|
if k not in special_k:
|
|
partAs = connection_all[k][:, 0]
|
|
partBs = connection_all[k][:, 1]
|
|
indexA, indexB = np.array(limbSeq[k]) - 1
|
|
|
|
for i in range(len(connection_all[k])): # = 1:size(temp,1)
|
|
found = 0
|
|
subset_idx = [-1, -1]
|
|
for j, row in enumerate(subset): # 1:size(subset,1):
|
|
if row[indexA] == partAs[i] or row[indexB] == partBs[i]:
|
|
subset_idx[found] = j
|
|
found += 1
|
|
|
|
if found == 1:
|
|
j = subset_idx[0]
|
|
if subset[j][indexB] != partBs[i]:
|
|
subset[j][indexB] = partBs[i]
|
|
subset[j][-1] += 1
|
|
subset[j][-2] += (
|
|
candidate[partBs[i].astype(int), 2]
|
|
+ connection_all[k][i][2]
|
|
)
|
|
elif found == 2: # if found 2 and disjoint, merge them
|
|
j1, j2 = subset_idx
|
|
membership = (
|
|
(subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int)
|
|
)[:-2]
|
|
if len(np.nonzero(membership == 2)[0]) == 0: # merge
|
|
subset[j1][:-2] += subset[j2][:-2] + 1
|
|
subset[j1][-2:] += subset[j2][-2:]
|
|
subset[j1][-2] += connection_all[k][i][2]
|
|
subset = np.delete(subset, j2, 0)
|
|
else: # as like found == 1
|
|
subset[j1][indexB] = partBs[i]
|
|
subset[j1][-1] += 1
|
|
subset[j1][-2] += (
|
|
candidate[partBs[i].astype(int), 2]
|
|
+ connection_all[k][i][2]
|
|
)
|
|
|
|
# if find no partA in the subset, create a new subset
|
|
elif not found and k < 17:
|
|
row = -1 * np.ones(20)
|
|
row[indexA] = partAs[i]
|
|
row[indexB] = partBs[i]
|
|
row[-1] = 2
|
|
row[-2] = (
|
|
sum(candidate[connection_all[k][i, :2].astype(int), 2])
|
|
+ connection_all[k][i][2]
|
|
)
|
|
subset = np.vstack([subset, row])
|
|
# delete some rows of subset which has few parts occur
|
|
deleteIdx = []
|
|
|
|
for i, s in enumerate(subset):
|
|
if s[-1] < 4 or s[-2] / s[-1] < 0.4:
|
|
deleteIdx.append(i)
|
|
subset = np.delete(subset, deleteIdx, axis=0)
|
|
|
|
# subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
|
|
# candidate: x, y, score, id
|
|
return candidate, subset
|