介绍
opencv除了支持常用的物体检测模型和分类模型之外，还支持openpose模型，同样是线下训练和线上调用。这里不做特别多的介绍，先把源代码和数据放出来～
实验模型获取地址：https://github.com/CMU-Perceptual-Computing-Lab/openpose
基于body数据的代码实现:
import cv2
import time
import numpy as np
from random import randint image1 = cv2.imread("E:\\usb_test\\example\\yolov3\\OpenPose-Multi-Person\\111.jpg") protoFile = "E:\\usb_test\\example\\yolov3\\OpenPose-Multi-Person\\pose\\body_25\\pose_deploy.prototxt"
weightsFile = "E:\\usb_test\\example\\yolov3\\OpenPose-Multi-Person\\pose\\body_25\\pose_iter_584000.caffemodel"
nPoints = 25
# COCO Output Format
keypointsMapping = ['Nose', 'Neck', 'RShoulder', 'RElbow', 'RWrist', 'LShoulder', 'LElbow', 'LWrist', 'MidHip', 'RHip', 'RKnee',
'RAnkle', 'LHip', 'LKnee', 'LAnkle', 'REye', 'LEye', 'REar', "LEar", 'LBigToe', 'LSmallToe', "LHeel", 'RBigToe', 'RSmallToe' ,
'RHeel'] POSE_PAIRS = [[1,8], [1,2], [1,5], [2,3], [3,4], [5,6],
[6,7],[8,9],[9,10],[10,11], [8,12], [12,13],
[13,14], [1,0], [0,15],[15,17],[0,16],[16,18],
[2,17], [5,18], [14,19], [19,20], [14,21],[11,22],
[22,23] ,[11,24]] # 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 = [[26,27], [40,41], [48,49], [42,43], [44,45], [50,51],
[52,53], [32,33], [28,29], [30,31], [34,35],[36,37],
[38,39], [56,57], [58,59], [62,63], [60,61], [64,65],
[46,47], [54,55], [66,67], [68,69], [70,71],[72,73],
[74,75],[76,77]] colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0],
[255, 255, 0], [170, 255, 0], [85, 255, 0],
[0, 255, 0], [0, 255, 85], [0, 255, 170],
[0, 255, 255], [0, 170, 255], [0, 85, 255],
[0, 0, 255], [85, 0, 255], [170, 0, 255],
[255, 0, 255], [255, 0, 170], [255, 0, 85],
[255, 170, 85], [255, 170, 170], [255, 170, 255],
[255, 85, 85], [255, 85, 170], [255, 85, 255],
[170, 170, 170]] def getKeypoints(probMap, threshold=0.1): mapSmooth = cv2.GaussianBlur(probMap,(3,3),0,0) mapMask = np.uint8(mapSmooth>threshold)
keypoints = [] #find the blobs
_, contours, hierarchy = cv2.findContours(mapMask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #for each blob find the maxima
for cnt in contours:
#print(cnt)
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 = 15
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)) # Find the keypoints for the first and second limb
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:
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 # 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
personwiseKeypoints = -1 * np.ones((0, 26)) 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
for j in range(len(personwiseKeypoints)):
if personwiseKeypoints[j][indexA] == partAs[i]:
person_idx = j
found = 1
break
print("find",found)
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
elif not found and k < 24:
row = -1 * np.ones(26)
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 frameWidth = image1.shape[1]
frameHeight = image1.shape[0] t = time.time()
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile) # Fix the input Height and get the width according to the Aspect Ratio
inHeight = 360
inWidth = int((inHeight/frameHeight)*frameWidth) inpBlob = cv2.dnn.blobFromImage(image1, 1.0 / 255, (inWidth, inHeight),
(0, 0, 0), swapRB=False, crop=False)
print("", inpBlob.shape )
net.setInput(inpBlob)
#net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
#net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)
output = net.forward()
print(output.shape)
print("Time Taken in forward pass = {}".format(time.time() - t)) 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, (image1.shape[1], image1.shape[0]))
keypoints = getKeypoints(probMap, threshold)
print("Keypoints - {} : {}".format(keypointsMapping[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)
print("detected_keypoints",detected_keypoints) frameClone = image1.copy()
for i in range(nPoints):
for j in range(len(detected_keypoints[i])):
cv2.circle(frameClone, detected_keypoints[i][j][0:2], 3, colors[i], -1, cv2.LINE_AA)
cv2.imshow("Keypoints",frameClone) valid_pairs, invalid_pairs = getValidPairs(output)
personwiseKeypoints = getPersonwiseKeypoints(valid_pairs, invalid_pairs) for i in range(24):
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], 2, cv2.LINE_AA) cv2.imshow("Detected Pose" , frameClone)
cv2.waitKey(0)
实验效果
