人工智能开发系列(7) OPENPOSE开发与实现 - Toybrick

import cv2
import time
import numpy as np
from random import randint
from rknn.api import RKNN
rknn = RKNN()
'''
protoFile = "pose/coco/pose_deploy_linevec.prototxt"
weightsFile = "pose/coco/pose_iter_440000.caffemodel"
'''
nPoints = 18
# COCO Output Format
keypointsMapping = ['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']
POSE_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],
[2,16], [5,17] ]
# index of pafs correspoding to the POSE_PAIRS
# e.g for POSE_PAIR(1,2), the PAFs are located at indices (31,32) of output, Similarly, (1,5) -> (39,40) and so on.
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]]
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]]
def getKeypoints(probMap, threshold=0.1):
mapSmooth = cv2.GaussianBlur(probMap,(3,3),0,0)
mapMask = np.uint8(mapSmooth>threshold)
#np.set_printoptions(threshold=np.inf)
keypoints = []
#find the blobs
_, 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，对confidence map 应用一个阀值（本例采用0.1），生成二值图。
#首先找出每个关键点区域的全部轮廓。
#生成这个区域的mask。
#通过用probMap乘以这个mask，提取该区域的probMap。
#找到这个区域的本地极大值。要对每个即关键点区域进行处理。
#本地极大值对应的坐标就是关键点坐标
for cnt in contours:
blobMask = np.zeros(mapMask.shape)
blobMask = cv2.fillConvexPoly(blobMask, cnt, 1)
maskedProbMap = mapSmooth * blobMask
_, maxVal, _, maxLoc = cv2.minMaxLoc(maskedProbMap)
keypoints.append(maxLoc + (probMap[maxLoc[1], maxLoc[0]],))
return keypoints
# Find valid connections between the different joints of a all persons present
def getValidPairs(output):
valid_pairs = []
invalid_pairs = []
n_interp_samples = 10
paf_score_th = 0.1
conf_th = 0.7
# loop for every POSE_PAIR
for k in range(len(mapIdx)):
# A->B constitute a limb
pafA = output[0, mapIdx[k][0], :, :]
pafB = output[0, mapIdx[k][1], :, :]
pafA = cv2.resize(pafA, (frameWidth, frameHeight))
pafB = cv2.resize(pafB, (frameWidth, frameHeight))
# candA: (124, 365, 0.17102814, 43)
# detected_keypoints keypoint_id
# Find the keypoints for the first and second limb
#把连接对上的关键点提取出来，相同的关键点放一起。把关键点对分开地方到两个列表上
#（列表名为candA和candB）。在列表candA上的每一个点都会和列表candB上某些点连接
candA = detected_keypoints[POSE_PAIRS[k][0]]
candB = detected_keypoints[POSE_PAIRS[k][1]]
nA = len(candA)
nB = len(candB)
# If keypoints for the joint-pair is detected
# check every joint in candA with every joint in candB
# Calculate the distance vector between the two joints
# Find the PAF values at a set of interpolated points between the joints
# Use the above formula to compute a score to mark the connection valid
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
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)
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
paf_scores = np.dot(paf_interp, d_ij)
avg_paf_score = sum(paf_scores)/len(paf_scores)
# Check if the connection is valid
# If the fraction of interpolated vectors aligned with PAF is higher then threshold -> Valid Pair
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:
# detected_keypoints keypoint_id
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
invalid_pairs.append(k)
valid_pairs.append([])
return valid_pairs, invalid_pairs
# This function creates a list of keypoints belonging to each person
# For each detected valid pair, it assigns the joint(s) to a person
def getPersonwiseKeypoints(valid_pairs, invalid_pairs):
# the last number in each row is the overall score
#我们首先创建空列表，用来存放每个人的关键点（即关键部位）
personwiseKeypoints = -1 * np.ones((0, 19))
for k in range(len(mapIdx)):
if k not in invalid_pairs:
partAs = valid_pairs[k][:,0]
partBs = valid_pairs[k][:,1]
indexA, indexB = np.array(POSE_PAIRS[k])
for i in range(len(valid_pairs[k])):
found = 0
person_idx = -1
#遍历每一个连接对，检查连接对中的partA是否已经存在于任意列表之中
for j in range(len(personwiseKeypoints)):
if personwiseKeypoints[j][indexA] == partAs[i]:
person_idx = j
found = 1
break
#如果存在，那么意味着这关键点属于当前列表，同时连接对中的partB也同样属于这个人体
#把连接对中的partB增加到partA所在的列表。
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不存在于任意列表，那么说明这一对属于一个还没建立列表的人体，于是需要新建一个新列表。
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
inWidth = 368
inHeight = 368
rknn.load_rknn('./pose_deploy_linevec_pre_compile.rknn')
ret = rknn.init_runtime()
if ret != 0:
print('Init runtime environment failed')
exit(ret)
print('done')
cap = cv2.VideoCapture(0)
hasFrame, frame = cap.read()
while cv2.waitKey(1) < 0:
t = time.time()
hasFrame, frame = cap.read()
# resize输入图像为368x368
frame = cv2.resize(frame, (inWidth, inHeight), interpolation=cv2.INTER_CUBIC)
if not hasFrame:
cv2.waitKey()
break
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
# input mode转为’nchw’
frame_input = np.transpose(frame, [2, 0, 1])
t = time.time()
[output] = rknn.inference(inputs=[frame_input], data_format="nchw")
print("time:", time.time()-t)
# rknn输出的数组转为1x57x46x46的矩阵
output = output.reshape(1, 57, 46, 46)
detected_keypoints = []
keypoints_list = np.zeros((0,3))
keypoint_id = 0
threshold = 0.1
for part in range(nPoints):
probMap = output[0,part,:,:]
probMap = cv2.resize(probMap, (frame.shape[1], frame.shape[0]))
keypoints = getKeypoints(probMap, threshold)
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)
frameClone = frame.copy()
#for i in range(nPoints):
# for j in range(len(detected_keypoints[i])):
# cv2.circle(frameClone, detected_keypoints[i][j][0:2], 5, colors[i], -1, cv2.LINE_AA)
#cv2.imshow("Keypoints",frameClone)
valid_pairs, invalid_pairs = getValidPairs(output)
personwiseKeypoints = getPersonwiseKeypoints(valid_pairs, invalid_pairs)
#连接各个人体关键点
for i in range(17):
for n in range(len(personwiseKeypoints)):
index = personwiseKeypoints[n][np.array(POSE_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(frameClone, (B[0], A[0]), (B[1], A[1]), colors[i], 3, cv2.LINE_AA)
cv2.imshow("Detected Pose" , frameClone)
#cv2.waitKey(0)
rknn.release()