opencv로 동영상 자르기+ YOLO 적용

 

1. 일단 opencv로 동영상을 잘라준다

2. yolo 적용해서 object detection 감지 ( 아래 사이트 참조)

coco.names

0.00MB

yolov3.cfg

0.01MB

yolo_object_detection.zip

2.76MB

* yoloc3.weighs 는 아래에서 다운로드받아서 쓰기 

YOLO object detection using Opencv with Python - Pysource

 

mp4_cctv_list=[]
for i in file_list:
    # 현재 디렉토리에 있는 모든 파일 리스트를 가져온다
    
    path = "./task1/"+str(i)
    file_listttt = os.listdir(path)
    mp4_cctv = [file for file in file_listttt if file.endswith(".mp4")]
    mp4_cctv_list.append(mp4_cctv)


for j in range(1,len(file_list)):
    mp4_cctv = "./task1/"+str(file_list[j-1])+"/"+str(mp4_cctv_list[j-1][0])

    try : 
        # -------------------- 동영상 쪼개기 ------------------------------------------------------------   
        import cv2
        n=119  #동영상을 119개로 쪼개줄것임

        vidcap = cv2.VideoCapture(mp4_cctv)     
        total_frames = vidcap.get(cv2.CAP_PROP_FRAME_COUNT)
        frames_step = total_frames//n
        for i in range(n):
            #here, we set the parameter 1 which is the frame number to the frame (i*frames_step)
            vidcap.set(1,i*frames_step)
            success,image = vidcap.read()  
            #save your image
            globals()['col{}.jpg'.format(i)]= image
            #cv2.imwrite(globals()['./col{}.jpg'.format(i)],image)
            
            # 저장해줄 위치 지정해줌
            cv2.imwrite('./new2/col'+str(i)+'.jpg',image)
        vidcap.release()
        
        # -------------------- yolo ------------------------------------------------------------
        # Yolo 로드
        net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
        classes = []
        with open("coco.names", "r") as f:
            classes = [line.strip() for line in f.readlines()]
        layer_names = net.getLayerNames()
        output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
        colors = np.random.uniform(0, 255, size=(len(classes), 3))

        for k in range(119):  #수정 119로
            # 이미지 가져오기
            print('사진' ,k)
            img = cv2.imread("./new2/col"+str(k)+".jpg")
            img = cv2.resize(img, None, fx=0.4, fy=0.4)
            height, width, channels = img.shape

            # Detecting objects
            blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
            net.setInput(blob)
            outs = net.forward(output_layers)

            # 정보를 화면에 표시

            class_ids = []
            confidences = []
            boxes = []
            for out in outs:
                for detection in out:
                    scores = detection[5:]
                    class_id = np.argmax(scores)
                    confidence = scores[class_id]
                    if confidence > 0.5:
                        # Object detected
                        center_x = int(detection[0] * width)
                        center_y = int(detection[1] * height)
                        w = int(detection[2] * width)
                        h = int(detection[3] * height)
                        # 좌표
                        x = int(center_x - w / 2)
                        y = int(center_y - h / 2)
                        boxes.append([x, y, w, h])
                        confidences.append(float(confidence))
                        class_ids.append(class_id)

            indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

            font = cv2.FONT_HERSHEY_PLAIN
            label_lists=[]
            for i in range(len(boxes)):
                if i in indexes:
                    x, y, w, h = boxes[i]
                    label = str(classes[class_ids[i]])
                    color = colors[i]
                    cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
                    cv2.putText(img, label, (x, y + 30), font, 3, color, 3)
                    label_lists.append(label)

            cv2.imshow("Image", img) 
            cv2.waitKey(1)
            cv2.destroyAllWindows()
            print(label_lists)
            df['mp4'][:][k]=label_lists


        #맥에서 opencv 안닫힐때 꿀팁
        cv2.destroyAllWindows()
        cv2.waitKey(1)
        cv2.waitKey(1)
        cv2.waitKey(1)
        cv2.waitKey(1)

 
    except :
        print('exept',file_list[j-1],j-1)