MyCity/smart_home/pose_detector.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] ]


frame = cv2.imread("single.jpeg")
frameCopy = np.copy(frame)
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
threshold = 0.2

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', cv2.resize(frameCopy,(900,900)))
cv2.imshow('Output-Skeleton', cv2.resize(frame,(600,900)))


cv2.imwrite('Output-Keypoints.jpg', frameCopy)
cv2.imwrite('Output-Skeleton.jpg', frame)

print("Total time taken : {:.3f}".format(time.time() - t))

cv2.waitKey(0)