Human Detect Module

+# 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)
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