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OpenPoseImage.py

+import cv2
+import time
+import numpy as np
+
+MODE = "COCO"
+
+if MODE is "COCO":
+    protoFile = "pose/coco/pose_deploy_linevec.prototxt"
+    weightsFile = "pose/coco/pose_iter_440000.caffemodel"
+    nPoints = 18
+    POSE_PAIRS = [ [1,0],[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[0,14],[0,15],[14,16],[15,17]]
+
+elif MODE is "MPI" :
+    protoFile = "pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt"
+    weightsFile = "pose/mpi/pose_iter_160000.caffemodel"
+    nPoints = 15
+    POSE_PAIRS = [[0,1], [1,2], [2,3], [3,4], [1,5], [5,6], [6,7], [1,14], [14,8], [8,9], [9,10], [14,11], [11,12], [12,13] ]
+
+
+frame = cv2.imread("single.jpeg")
+frameCopy = np.copy(frame)
+frameWidth = frame.shape[1]
+frameHeight = frame.shape[0]
+threshold = 0.1
+
+net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
+
+t = time.time()
+# input image dimensions for the network
+inWidth = 368
+inHeight = 368
+inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),
+                          (0, 0, 0), swapRB=False, crop=False)
+
+net.setInput(inpBlob)
+
+output = net.forward()
+print("time taken by network : {:.3f}".format(time.time() - t))
+
+H = output.shape[2]
+W = output.shape[3]
+
+# Empty list to store the detected keypoints
+points = []
+
+for i in range(nPoints):
+    # confidence map of corresponding body's part.
+    probMap = output[0, i, :, :]
+
+    # Find global maxima of the probMap.
+    minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
+    
+    # Scale the point to fit on the original image
+    x = (frameWidth * point[0]) / W
+    y = (frameHeight * point[1]) / H
+
+    if prob > threshold : 
+        cv2.circle(frameCopy, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)
+        cv2.putText(frameCopy, "{}".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)
+
+        # Add the point to the list if the probability is greater than the threshold
+        points.append((int(x), int(y)))
+    else :
+        points.append(None)
+
+# Draw Skeleton
+for pair in POSE_PAIRS:
+    partA = pair[0]
+    partB = pair[1]
+
+    if points[partA] and points[partB]:
+        cv2.line(frame, points[partA], points[partB], (0, 255, 255), 2)
+        cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
+
+
+cv2.imshow('Output-Keypoints', frameCopy)
+cv2.imshow('Output-Skeleton', frame)
+
+
+cv2.imwrite('Output-Keypoints.jpg', frameCopy)
+cv2.imwrite('Output-Skeleton.jpg', frame)
+
+print("Total time taken : {:.3f}".format(time.time() - t))
+
+cv2.waitKey(0)
+

OpenPoseVideo.py

+import cv2
+import time
+import numpy as np
+
+MODE = "MPI"
+
+if MODE is "COCO":
+    protoFile = "pose/coco/pose_deploy_linevec.prototxt"
+    weightsFile = "pose/coco/pose_iter_440000.caffemodel"
+    nPoints = 18
+    POSE_PAIRS = [ [1,0],[1,2],[1,5],[2,3],[3,4],[5,6],[6,7],[1,8],[8,9],[9,10],[1,11],[11,12],[12,13],[0,14],[0,15],[14,16],[15,17]]
+
+elif MODE is "MPI" :
+    protoFile = "pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt"
+    weightsFile = "pose/mpi/pose_iter_160000.caffemodel"
+    nPoints = 15
+    POSE_PAIRS = [[0,1], [1,2], [2,3], [3,4], [1,5], [5,6], [6,7], [1,14], [14,8], [8,9], [9,10], [14,11], [11,12], [12,13] ]
+
+
+inWidth = 368
+inHeight = 368
+threshold = 0.1
+
+
+input_source = "output-roy.avi"
+cap = cv2.VideoCapture(input_source)
+hasFrame, frame = cap.read()
+
+vid_writer = cv2.VideoWriter('output.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 10, (frame.shape[1],frame.shape[0]))
+
+net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
+
+while cv2.waitKey(1) < 0:
+    t = time.time()
+    hasFrame, frame = cap.read()
+    frameCopy = np.copy(frame)
+    if not hasFrame:
+        cv2.waitKey()
+        break
+
+    frameWidth = frame.shape[1]
+    frameHeight = frame.shape[0]
+
+    inpBlob = cv2.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),
+                              (0, 0, 0), swapRB=False, crop=False)
+    net.setInput(inpBlob)
+    output = net.forward()
+
+    H = output.shape[2]
+    W = output.shape[3]
+    # Empty list to store the detected keypoints
+    points = []
+
+    for i in range(nPoints):
+        # confidence map of corresponding body's part.
+        probMap = output[0, i, :, :]
+
+        # Find global maxima of the probMap.
+        minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
+        
+        # Scale the point to fit on the original image
+        x = (frameWidth * point[0]) / W
+        y = (frameHeight * point[1]) / H
+
+        if prob > threshold : 
+            cv2.circle(frameCopy, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)
+            cv2.putText(frameCopy, "{}".format(i), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, lineType=cv2.LINE_AA)
+
+            # Add the point to the list if the probability is greater than the threshold
+            points.append((int(x), int(y)))
+        else :
+            points.append(None)
+
+    # Draw Skeleton
+    for pair in POSE_PAIRS:
+        partA = pair[0]
+        partB = pair[1]
+
+        if points[partA] and points[partB]:
+            cv2.line(frame, points[partA], points[partB], (0, 255, 255), 3, lineType=cv2.LINE_AA)
+            cv2.circle(frame, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
+            cv2.circle(frame, points[partB], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
+
+    cv2.putText(frame, "time taken = {:.2f} sec".format(time.time() - t), (50, 50), cv2.FONT_HERSHEY_COMPLEX, .8, (255, 50, 0), 2, lineType=cv2.LINE_AA)
+    # cv2.putText(frame, "OpenPose using OpenCV", (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 50, 0), 2, lineType=cv2.LINE_AA)
+    # cv2.imshow('Output-Keypoints', frameCopy)
+    cv2.imshow('Output-Skeleton', frame)
+
+    vid_writer.write(frame)
+
+vid_writer.release()

getModels.sh

+# ------------------------- BODY, FACE AND HAND MODELS -------------------------
+# Downloading body pose (COCO and MPI), face and hand models
+OPENPOSE_URL="http://posefs1.perception.cs.cmu.edu/OpenPose/models/"
+POSE_FOLDER="pose/"
+FACE_FOLDER="face/"
+HAND_FOLDER="hand/"
+
+# ------------------------- POSE MODELS -------------------------
+# Body (COCO)
+COCO_FOLDER=${POSE_FOLDER}"coco/"
+COCO_MODEL=${COCO_FOLDER}"pose_iter_440000.caffemodel"
+wget -c ${OPENPOSE_URL}${COCO_MODEL} -P ${COCO_FOLDER}
+# Alternative: it will not check whether file was fully downloaded
+# if [ ! -f $COCO_MODEL ]; then
+#     wget ${OPENPOSE_URL}$COCO_MODEL -P $COCO_FOLDER
+# fi
+
+# Body (MPI)
+MPI_FOLDER=${POSE_FOLDER}"mpi/"
+MPI_MODEL=${MPI_FOLDER}"pose_iter_160000.caffemodel"
+wget -c ${OPENPOSE_URL}${MPI_MODEL} -P ${MPI_FOLDER}
+
+# "------------------------- FACE MODELS -------------------------"
+# Face
+FACE_MODEL=${FACE_FOLDER}"pose_iter_116000.caffemodel"
+wget -c ${OPENPOSE_URL}${FACE_MODEL} -P ${FACE_FOLDER}
+
+# "------------------------- HAND MODELS -------------------------"
+# Hand
+HAND_MODEL=$HAND_FOLDER"pose_iter_102000.caffemodel"
+wget -c ${OPENPOSE_URL}${HAND_MODEL} -P ${HAND_FOLDER}