运行截图：
原文链接：https://www.aiuai.cn/aifarm946.html
源码：
#!/usr/bin/python3
#!--*-- coding: utf-8 --*--
import os
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
class general_mulitpose_model(object):
def __init__(self):
self.point_names = ['Nose', 'Neck',
'R-Sho', 'R-Elb', 'R-Wr',
'L-Sho', 'L-Elb', 'L-Wr',
'R-Hip', 'R-Knee', 'R-Ank',
'L-Hip', 'L-Knee', 'L-Ank',
'R-Eye', 'L-Eye', 'R-Ear', 'L-Ear']
self.point_pairs = [[1,2], [1,5], [2,3], [3,4], [5,6], [6,7],
[1,8], [8,9], [9,10], [1,11], [11,12], [12,13],
[1,0], [0,14], [14,16], [0,15], [15,17],
[2,17], [5,16] ]
# index of pafs correspoding to the self.point_pairs
# e.g for point_pairs(1,2), the PAFs are located at indices (31,32) of output,
# Similarly, (1,5) -> (39,40) and so on.
self.map_idx = [[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]]
self.colors = [[0,100,255], [0,100,255], [0,255,255],
[0,100,255], [0,255,255], [0,100,255],
[0,255,0], [255,200,100], [255,0,255],
[0,255,0], [255,200,100], [255,0,255],
[0,0,255], [255,0,0], [200,200,0],
[255,0,0], [200,200,0], [0,0,0]]
self.num_points = 18
self.pose_net = self.get_model()
def get_model(self):
prototxt = "./models/pose/coco/pose_deploy_linevec.prototxt"
caffemodel = "./models/pose/coco/pose_iter_440000.caffemodel"
coco_net = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)
return coco_net
def getKeypoints(self, probMap, threshold=0.1):
mapSmooth = cv2.GaussianBlur(probMap, (3, 3), 0, 0)
mapMask = np.uint8(mapSmooth > threshold)
keypoints = []
# find the blobs找出对应于关键点的所有区域的轮廓(contours)
contours, hierarchy = cv2.findContours(mapMask,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# for each blob find the maxima
# 对于每个关键点轮廓区域，找到最大值.
for cnt in contours:
blobMask = np.zeros(mapMask.shape) # 创建关键点的 mask；
blobMask = cv2.fillConvexPoly(blobMask, cnt, 1)
maskedProbMap = mapSmooth * blobMask # 提取关键点区域的 probMap
_, maxVal, _, maxLoc = cv2.minMaxLoc(maskedProbMap)# 提取关键点区域的局部最大值
keypoints.append(maxLoc + (probMap[maxLoc[1], maxLoc[0]],))
return keypoints
def getValidPairs(self, output, detected_keypoints, img_width, img_height):
"""有效关键点(joint pair) 对检测是指两个关键点的连接，判断是否属于相同的人体.
通过计算一个关键点与其它所有可能的关键点之间的最小距离，来判断关键点对的有效性.
如果缺失：
Part Affinity Maps给定沿着两个关键点对的仿射(affinity)的方向
有效的关键点对不仅具有最小的距离，其方向也应该顺着 PAF Heatmaps 方向
"""
valid_pairs = []
invalid_pairs = []
n_interp_samples = 10
paf_score_th = 0.1
conf_th = 0.7
for k in range(len(self.map_idx)):
# A->B constitute a limb关键点
pafA = output[0, self.map_idx[k][0], :, :]
pafB = output[0, self.map_idx[k][1], :, :]
pafA = cv2.resize(pafA, (img_width, img_height))
pafB = cv2.resize(pafB, (img_width, img_height))
# Find the keypoints for the first and second limb
# 检测第一个 limb 和第二个 limb 的关键点位置
candA = detected_keypoints[self.point_pairs[k][0]]
candB = detected_keypoints[self.point_pairs[k][1]]
nA = len(candA)
nB = len(candB)
# 如果检测到 joint-pair 的关键点位置，则，
# 检查 candA 和 candB 中每个 joint.
# 计算两个 joints 之间的距离向量(distance vector).
# 计算两个 joints 之间插值点集合的 PAF 值.
# 根据上述公式，计算 score 值，判断连接的有效性.
if (nA != 0 and nB != 0):
valid_pair = np.zeros((0, 3))
for i in range(nA):
max_j = -1
maxScore = -1
found = 0
for j in range(nB):
# Find d_ij计算两个关键点之间的单位向量，其给定了关节点之间连线的方向.
d_ij = np.subtract(candB[j][:2], candA[i][:2])
norm = np.linalg.norm(d_ij)
if norm:
d_ij = d_ij / norm
else:
continue
# Find p(u)计算两个关键点之间连线的 10 个插值点.
interp_coord = list(
zip(np.linspace(candA[i][0], candB[j][0], num=n_interp_samples),
np.linspace(candA[i][1], candB[j][1], num=n_interp_samples)))
# Find L(p(u))
paf_interp = []
for k in range(len(interp_coord)):
paf_interp.append([pafA[int(round(interp_coord[k][1])), int(
round(interp_coord[k][0]))],
pafB[int(round(interp_coord[k][1])), int(
round(interp_coord[k][0]))]])
# Find E计算插值点的 PAF 与单位向量 d_ij 之间的点积(dot product).
paf_scores = np.dot(paf_interp, d_ij)
avg_paf_score = sum(paf_scores) / len(paf_scores)
# Check if the connection is valid
## 判断连接是否有效.
# 如果对应于 PAF 的插值向量值大于阈值，则连接有效.
# 如果这些插值点的 70% 的都满足判定标准，则该关键点对是有效的.
if (len(np.where(paf_scores > paf_score_th)[
0]) / n_interp_samples) > conf_th:
if avg_paf_score > maxScore:
max_j = j
maxScore = avg_paf_score
found = 1
# Append the connection to the list
if found:
valid_pair = np.append(valid_pair,
[[candA[i][3], candB[max_j][3], maxScore]], axis=0)
# Append the detected connections to the global list
valid_pairs.append(valid_pair)
else: # If no keypoints are detected
print("No Connection : k = {}".format(k))
invalid_pairs.append(k)
valid_pairs.append([])
return valid_pairs, invalid_pairs
def getPersonwiseKeypoints(self, valid_pairs, invalid_pairs, keypoints_list):
"""同一人体关键点的组合
对于每个检测到的有效 joint pair，分配属于一个人体的 joints.
"""
# 空列表 每一行的最后一个值为 overall score.
personwiseKeypoints = -1 * np.ones((0, 19))
#对每个关键点对，判断 partA 是否已经在列表里,
#如果已经在列表里，则表示该关键点对属于该列表，且 partB 也属于同一人体.
#因此，添加该关键点对的 partB 到 partA 所在的列表.
for k in range(len(self.map_idx)):
if k not in invalid_pairs:
partAs = valid_pairs[k][:, 0]
partBs = valid_pairs[k][:, 1]
indexA, indexB = np.array(self.point_pairs[k])
for i in range(len(valid_pairs[k])):
found = 0
person_idx = -1
for j in range(len(personwiseKeypoints)):
if personwiseKeypoints[j][indexA] == partAs[i]:
person_idx = j
found = 1
break
if found:
personwiseKeypoints[person_idx][indexB] = partBs[i]
personwiseKeypoints[person_idx][-1] += keypoints_list[
partBs[i].astype(int), 2] + \
valid_pairs[k][i][2]
# if find no partA in the subset, create a new subset
#如果 partA 不在任一人体列表里，则表示该关键点对属于一个新出现的人体，故创建新的列表.
elif not found and k < 17:
row = -1 * np.ones(19)
row[indexA] = partAs[i]
row[indexB] = partBs[i]
# add the keypoint_scores for the two keypoints and the paf_score
row[-1] = sum(keypoints_list[valid_pairs[k][i, :2].astype(int), 2]) + \
valid_pairs[k][i][2]
personwiseKeypoints = np.vstack([personwiseKeypoints, row])
return personwiseKeypoints
def predict(self, imgfile):
img_cv2 = cv2.imread(imgfile)
img_width, img_height = img_cv2.shape[1], img_cv2.shape[0]
net_height = 368
net_width = int((net_height / img_height) * img_width)
start = time.time()
in_blob = cv2.dnn.blobFromImage(
img_cv2,
1.0 / 255,
(net_width, net_height),
(0, 0, 0),
swapRB=False,
crop=False)
self.pose_net.setInput(in_blob)
output = self.pose_net.forward()
print("[INFO]Time Taken in Forward pass: {}".format(time.time() - start))
detected_keypoints = []
keypoints_list = np.zeros((0, 3))
keypoint_id = 0
threshold = 0.1
for part in range(self.num_points):
probMap = output[0, part, :, :]
probMap = cv2.resize(probMap, (img_cv2.shape[1], img_cv2.shape[0]))
keypoints = self.getKeypoints(probMap, threshold)
print("Keypoints - {} : {}".format(self.point_names[part], keypoints))
keypoints_with_id = []
for i in range(len(keypoints)):
keypoints_with_id.append(keypoints[i] + (keypoint_id,))
keypoints_list = np.vstack([keypoints_list, keypoints[i]])
keypoint_id += 1
detected_keypoints.append(keypoints_with_id)
valid_pairs, invalid_pairs = \
self.getValidPairs(output,
detected_keypoints,
img_width,
img_height)
personwiseKeypoints = \
self.getPersonwiseKeypoints(valid_pairs,
invalid_pairs,
keypoints_list)
return personwiseKeypoints, keypoints_list
def vis_pose(self, img_file, personwiseKeypoints, keypoints_list):
"""检测结果可视化
"""
img_cv2 = cv2.imread(img_file)
for i in range(17):
for n in range(len(personwiseKeypoints)):
index = personwiseKeypoints[n][np.array(self.point_pairs[i])]
if -1 in index:
continue
B = np.int32(keypoints_list[index.astype(int), 0])
A = np.int32(keypoints_list[index.astype(int), 1])
cv2.line(img_cv2, (B[0], A[0]), (B[1], A[1]), self.colors[i], 3, cv2.LINE_AA)
plt.figure()
plt.imshow(img_cv2[:, :, ::-1])
plt.title("Results")
plt.axis("off")
plt.show()
if __name__ == '__main__':
print("[INFO]MultiPose estimation.")
img_file = "images/multipose_test_image.jpg"
start = time.time()
multipose_model = general_mulitpose_model()
print("[INFO]Time Taken in Model Loading: {}".\
format(time.time() - start))
personwiseKeypoints, keypoints_list = \
multipose_model.predict(img_file)
multipose_model.vis_pose(img_file,
personwiseKeypoints,
keypoints_list)
print(personwiseKeypoints)
print(keypoints_list)
print("[INFO]Done.")
