[UPDATE] 구현 결과

 import math
+import time
 import cv2
 import numpy as np
 from copy import deepcopy
@@ -30,19 +31,27 @@ def get_classes(file):
 if __name__ == '__main__':
     # 파일 열기
     camera = cv2.VideoCapture("input/example.mp4")
+    if camera.isOpened() == False:
+        print("Can't open Video.")
 
     # Yolo 학습
     yolo = YOLO(0.6, 0.5)
     file = 'data/coco_classes.txt'
     all_classes = get_classes(file)
 
-    # 1 카운트 할 때마다 frame 얻어서 파일로 저장
-    success,image = camera.read()
+    # 10 카운트 할 때마다 frame 얻어서 파일로 저장
+    success, image = camera.read()
     count = 0
+    frameCount = 0
+    start_time = time.time()
     while success:
-        cv2.imwrite("mid/frame%d.png" % count, image)     # save frame as JPEG file
+        frameCount += 1
+        if frameCount % 3 == 0:
+            cv2.imwrite("mid/frame%d.png" % count, image)     # save frame as JPEG file
+            count += 1
         success, image = camera.read()
-        count += 1
+
+    camera.release()
 
     # 각 프레임 별로 Image Detection 후 프레임 번호, 객체 이름(name)과 객체의 크기(size), 객체가 얼마나 가운데 있는지(coordinatevalue) 저장
     detectionInfo = []
@@ -50,7 +59,6 @@ if __name__ == '__main__':
         #filename = "mid/frame"+str(i)+"."
         image = cv2.imread("mid/frame%d.png" % i)
         pimage = process_image(image)
-
         boxes, classes, scores = yolo.predict(pimage, image.shape)
         for box, score, cl in zip(boxes, scores, classes):
             x, y, w, h = box
@@ -61,7 +69,6 @@ if __name__ == '__main__':
                 continue
             if x <= 0 or x+w >= image.shape[1] or y <= 0 or y+h >= image.shape[0]: # 검출된 객체가 프레임 밖으로 나간 경우
                 continue
-
             # 얼마나 가운데인지 확인하는 알고리즘
             object = Point2D(width= x + w/2, height= y + h/2)
             a = image.shape[1]/2 - object.width
@@ -174,7 +181,8 @@ if __name__ == '__main__':
 
         # 두 객체가 겹쳤는지 좌표 비교를 통해 확인, 겹쳤다면 겹친 부분의 좌표 값 o_xxx 에 저장
         isOverlapped = False
-        if a_top < b_top < a_bottom or b_top < a_top < b_bottom or a_left < b_left < a_right or b_left < a_left < b_right:
+        if a_top < b_top < a_bottom or b_top < a_top < b_bottom \
+                or a_left < b_left < a_right or b_left < a_left < b_right:
             isOverlapped = True
             o_top = max(a_top, b_top)
             o_bottom = min(a_bottom, b_bottom)
@@ -187,11 +195,14 @@ if __name__ == '__main__':
         if isOverlapped == True:
             a_object = Point2D(width= (a_top + a_bottom)//2, height=(a_left + a_right)//2)
             b_object = Point2D(width= (b_top + b_bottom)//2, height=(b_left + b_right)//2)
-            o_object = Point2D(width=(a_object.width+b_object.width)//2 , height = (a_object.height + b_object.height)//2)
+            o_object = Point2D(width=(a_object.width+b_object.width)//2 , \
+                               height = (a_object.height + b_object.height)//2)
             toTheOrigin_w = image.shape[1] / 2 - o_object.width
             toTheOrigin_h = image.shape[0] / 2 - o_object.height
             coordiValue_object = int(math.sqrt((toTheOrigin_w ** 2) + (toTheOrigin_h ** 2)))
-            bestList[key] = [value[0][0] + ", "+ value[1][0], value[0][1]+value[1][1]-o_size, coordiValue_object, min(a_top, b_top), min(a_left, b_left), max(a_right, b_right), max(a_bottom, b_bottom)]
+            bestList[key] = [value[0][0] + ", "+ value[1][0], value[0][1]+value[1][1]-o_size, \
+                             coordiValue_object, min(a_top, b_top), min(a_left, b_left), \
+                             max(a_right, b_right), max(a_bottom, b_bottom)]
         else:
             # 겹치지 않았다면 목록에서 삭제
             deleteList.append(key)
@@ -250,7 +261,8 @@ if __name__ == '__main__':
                     ratio = int((image.shape[1]+padNum)/image.shape[1])
                     output = cv2.resize(output, dsize=(width, height), interpolation=cv2.INTER_LINEAR)
                 # 물체가 프레임의 중앙에 있을 경우
-                elif image.shape[1]//2 - image.shape[1]//20 < (crop_left + crop_right)// 2 <= image.shape[1]//2 + image.shape[1]//20:
+                elif image.shape[1]//2 - image.shape[1]//20 < (crop_left + crop_right)// 2 \
+                        <= image.shape[1]//2 + image.shape[1]//20:
                     crop_left = (crop_left + crop_right)//2 - width//2
                     crop_right = (crop_left + crop_right)//2 + width//2
                     output = image[crop_top:crop_bottom, crop_left:crop_right].copy()