modify 0421

@@ -7,36 +7,74 @@ hog = cv2.HOGDescriptor()
 hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
 
 cv2.startWindowThread()
-fname = "./croppedimg/"
+fname = "./croppedimg/human/"
 # open webcam video stream
 cap = cv2.VideoCapture(0)
 i = 0
+def draw_left_path(img,x,y,w,h):
+    start_point = x+w
+    cv2.line(img, (320-2*w,480), (start_point, y+int(h)), (255,0,0), 8) #4픽셀 선 그리기
+    cv2.line(img, (start_point, y+int(h)), (start_point+int(w/5),y+int(h-20)), (255, 0, 0), 10)
+    start_point = x+2*w
+    cv2.line(img, (540,480), (start_point, y+int(h)), (255,0,0), 8) #4픽셀 선 그리기
+   # cv2.line(img, (start_point, y+int(h)), (start_point+int(w/5),y+int(h-20)), (255, 0, 0), 10)
+    return img
+def draw_right_path(img, x, y, w, h):
+    start_point = x
+    cv2.line(img, (320+2*w, 480), (start_point, y+int(h)), (255, 0, 0), 8)  # 8픽셀 선 그리기
+    cv2.line(img, (start_point, y+int(h)), (start_point - int(w/5), y+int(h-20)), (255, 0, 0), 8)
+    start_point = x-2*w
+    cv2.line(img, (100, 480), (start_point, y + int(h)), (255, 0, 0), 8)  # 8픽셀 선 그리기
+   # cv2.line(img, (start_point, y + int(h)), (start_point - int(w / 5), y + int(h-20)), (255, 0, 0), 8)
+    return img
 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))
+    frame = cv2.resize(frame, (640, 480))
     # 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))
-
+    detectCount = 0
     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):
+        cv2.putText(frame, "Detect", (xA - 50, yA - 10), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+        detectCount = detectCount+1
+        if(detectCount>1):
+            print("Waiting...")
+        else :
+            if(i%10 == 0):
+                cropped = frame[yA:yB,xA:xB]
+                #print("xA : {0}, xB : {1}, yA : {2}, yB : {3}".format(xA, xB,yA,yB))  # Print Width, Height of Cropped Area
                 i=0
+            if(xB < 380 and xA<260):
+                print("Left Side Detect.")
+                try:
+                    frame = draw_left_path(frame, xA,yA,xB-xA,yB-yA)
+                except:
+                    pass
+            elif(xA>260 and xB>380):
+                print("Right Side Detect")
+                try:
+                    frame = draw_right_path(frame, xA, yA, xB - xA, yB - yA)
+                except:
+                    pass
+            else:
+                try:
+                    frame = draw_right_path(frame, xA, yA, xB - xA, yB - yA)
+                except:
+                    pass
+                print("Center Side Detect")
+        s = fname + str(i)+'.jpg'
+        cv2.imwrite(s, cropped) # IMG File Write
+        print("time :", time.time() - start)
         i= i+1
     # Display the resulting frame