HumanDetect.py
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# import the necessary packages
import numpy as np
import cv2
import time
# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
cv2.startWindowThread()
fname = "./croppedimg/"
# open webcam video stream
cap = cv2.VideoCapture(0)
i = 0
while (True):
# Capture frame-by-frame
start = time.time()
ret, frame = cap.read()
# resizing for faster detection[240,160] [320 * 240]
frame = cv2.resize(frame, (240, 160))
# using a greyscale picture, also for faster detection
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# detect people in the image
# returns the bounding boxes for the detected objects
boxes, weights = hog.detectMultiScale(frame, winStride=(8, 8))
boxes = np.array([[x, y, x + w, y + h] for (x, y, w, h) in boxes])
for (xA, yA, xB, yB) in boxes:
# display the detected boxes in the colour picture
cv2.rectangle(frame, (xA, yA), (xB, yB),
(0, 255, 0), 2)
if(i%10 == 0):
cropped = frame[yA:yB,xA:xB]
s = fname + str(i)+'.jpg'
cv2.imwrite(s, cropped) # IMG File Write
print("time :", time.time() - start)
print("Human Detect!") #Alert
if(i > 200):
i=0
i= i+1
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
# and release the output
# finally, close the window
cv2.destroyAllWindows()
cv2.waitKey(1)