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Pose_Image.py
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import collections
import time
import cv2
import numpy as np
# Mode Selection
MODE = "MPII"
# Common object in Context
if MODE == "COCO":
protoFile = r"./<path>/pose_deploy_linevec_faster_4_stages.prototxt"
weightsFile = r"./<path>/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]]
# Multi-person II
elif MODE == "MPII":
protoFile = r"./<path>/pose_deploy_linevec.prototxt"
weightsFile = r"./<path>/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]]
# User image input
userImageInput = r"./<path>/ak.jpg"
print(userImageInput)
# background frame
img = r"./<path>/White.jpg"
frame = cv2.imread(userImageInput)
print(frame.shape)
frameWhite = cv2.imread(img)
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
frameWidth1 = frameWhite.shape[1]
frameHeight1 = frameWhite.shape[0]
cv2.resize(frame, (480, 640))
cv2.resize(frameWhite, (520, 740))
threshold = 0.1
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)
t = time.time()
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("Total time taken by the network : {:.3f}".format(time.time() - t))
H = output.shape[2]
W = output.shape[3]
points = []
points1 = []
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 RunningImage
x = (frameWidth * point[0]) / W
y = (frameHeight * point[1]) / H
x1 = (frameWidth1 * point[0]) / W
y1 = (frameHeight1 * point[1]) / H
if prob > threshold:
cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
cv2.circle(frameWhite, (int(x1) + 70, int(y1)), 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)
points.append((int(x), int(y)))
points1.append((int(x1) + 70, int(y1)))
else:
points.append(None)
points1.append(None)
dictAngle = {'leftHand': [5, 6, 7], 'leftLeg': [11, 12, 13], 'rightHand': [2, 3, 4], 'rightLeg': [8, 9, 10]}
dictAngle = collections.OrderedDict(sorted(dictAngle.items()))
usernameDict = []
userangleDict = []
heightAngle = [350, 370, 390, 410]
j = 0
for i in dictAngle:
dictPoint1 = np.array(points[dictAngle[i][0]])
dictPoint2 = np.array(points[dictAngle[i][1]])
dictPoint3 = np.array(points[dictAngle[i][2]])
if str(dictPoint1) != 'None' and str(dictPoint2) != 'None' and str(dictPoint3) != 'None':
ba = dictPoint1 - dictPoint2
bc = dictPoint3 - dictPoint2
tup1 = points1[dictAngle[i][1]]
if i == 'leftHand' or i == 'leftLeg':
pointAngle = (tup1[0] + 15, tup1[1] + 18)
if i == 'rightHand' or i == 'rightLeg':
pointAngle = (tup1[0] - 50, tup1[1])
cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
angle = np.arccos(cosine_angle)
ang = str(np.degrees(angle))
angleFloat = float(ang)
ang1 = round(angleFloat, 2)
cv2.putText(frameWhite, "Right Side", (15, 40), cv2.FONT_HERSHEY_DUPLEX, 0.6, (0, 0, 0), 1, cv2.LINE_AA)
cv2.putText(frameWhite, "Left Side", (500, 40), cv2.FONT_HERSHEY_DUPLEX, 0.6, (0, 0, 0), 1, cv2.LINE_AA)
cv2.putText(frameWhite, "User-Image", (700, 420), cv2.FONT_HERSHEY_DUPLEX, 0.3, (0, 0, 0), 1, cv2.LINE_AA)
cv2.putText(frameWhite, str(ang1), pointAngle, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2, cv2.LINE_AA)
cv2.putText(frameWhite, str(i + ": "), (15, heightAngle[j]), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 0), 1,
cv2.LINE_AA)
cv2.putText(frameWhite, str(ang), (90, heightAngle[j]), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 0, 0), 1,
cv2.LINE_AA)
usernameDict.append(i)
userangleDict.append(ang)
else:
usernameDict.append(i)
userangleDict.append('0')
j += 1
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.line(frameWhite, points1[partA], points1[partB], (0, 255, 255), 2)
userDict = dict(zip(usernameDict, userangleDict))
pose_output = r"./<path>/ak_result.jpg"
cv2.imwrite(pose_output, frameWhite)
AngleDict = str(
userDict['rightHand'] + "," + userDict['leftLeg'] + "," + userDict['leftHand'] + "," + userDict['rightLeg'])
print("Executed Successfully")