yolo_scan_algorithm/camera_test.py at main · Profrog/yolo_scan_algorithm · GitHub

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from pyzbar import pyzbar
from datetime import datetime
import argparse
import cv2
import sys
import time
import os
import sys
import subprocess
import signal
import numpy as np


#test0


start2 = 0

net = cv2.dnn.readNet("yolov4.weights", "cfg/yolov4.cfg")
classes = []
with open("cfg/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))


# vc0 = cv2.VideoCapture(0)
#VideoCapture vc(0)
vc = cv2.VideoCapture(2) # 0은 노트북 웹캠 2는 usb로 연결된 웹캠

#vc.set(CV_CAP_PROP_FRAME_WIDTH,1920);
#vc.set(CV_CAP_PROP_FRAME_HEIGHT,1024);
#vc.set(CV_CAP_PROP_MODE, CV_CAP_MODE_YUYV);
#vc.set(CV_CAP_PROP_FPS, 60);


while True :
    #time.sleep(0.1)
    print("waiting...")


    if cv2.waitKey(1) & 0xFF == ord('q'):
        break


    if True:
        print("webcam start!")
        start = time.time()
        chk = 0
        vc.set(10,0.5)


        while time.time() - start <= 300 :

            # ret0, frame0 = vc0.read()
            # cv2.imshow("Video Window0", frame0)

            ret, frame = vc.read()
            cv2.imshow("Video Window", frame)
            cv2.imwrite('image.jpg',frame)

            image = cv2.imread('image.jpg')
            # image = cv2.resize(image, None, fx=0.4, fy=0.4)
            height, width, channels = image.shape

            barcodes = pyzbar.decode(image)

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

            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
            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(image, (x, y), (x + w, y + h), color, 2)
              cv2.putText(image, label, (x, y + 30), font, 3, color, 3)
              cv2.imshow("Image", image)

            # os.system('./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights image.jpg')
            # process = subprocess.Popen(..)   # pass cmd and args to the function
            # process.send_signal(signal.SIGINT)


            if cv2.waitKey(1) & 0xFF == ord('q'):
                break


        vc.release()
        cv2.destroyAllWindows()

        if chk == 1:
            print("access success")

        else:
            print("access failed")