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coco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\\coco\\pose_deploy_linevec.prototxt" weightsFile = "E:\\usb_test\\example\\yolov3\\OpenPose-Multi-Person\\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], [2,17], [5,16] ] # 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) 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 = 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)) # 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, 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 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 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 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 = 368 inWidth = int((inHeight/frameHeight)*frameWidth) inpBlob = cv2.dnn.blobFromImage(image1, 1.0 / 255, (inWidth, inHeight),(0, 0, 0), swapRB=False, crop=False) print("2222", inpBlob.shape ) net.setInput(inpBlob) 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) 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], 5, 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(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)