if False == cap.isOpened(): print(0)

As an AI language model, I can infer that this code snippet is checking if a video capture object named "cap" is not opened. If it is not opened, it prints 0.

相关问题

修改代码使其实现视频分辨率的调整， def run(self): while True: try: self.sock.connect(self.ADDR) break except: time.sleep(3) continue if self.showme: cv2.namedWindow('You',cv2.WINDOW_NORMAL) print('视频客户端已连接...') while self.cap.isOpened(): ret,frame = self.cap.read() if self.showme: cv2.imshow('You',frame) if cv2.waitKey(1) & 0xFF == 27: self.showme = False cv2.destroyWindow('You') #缩放 sframe =cv2.resize(frame,(0,0),fx=self.fx,fy=self.fx) data = pickle.dumps(sframe) zdata = zlib.compress(data,zlib.Z_BEST_COMPRESSION) try: self.sock.sendall(struct.pack("L",len(zdata))+zdata) except: break for i in range(self.interval): self.cap.read()

为了实现视频分辨率的调整，可以在客户端定义一个特定的消息类型，用于发送分辨率调整请求。具体实现步骤如下：

1. 在客户端代码中，定义一个函数用于发送分辨率调整请求，例如：

def send_resolution_request(self, resolution):
    # 定义一个特定的消息类型，用于发送分辨率调整请求
    msg_type = 1
    # 将分辨率参数打包成字节序列
    data = pickle.dumps(resolution)
    # 将消息类型和消息内容打包成一个字节序列
    msg = struct.pack('!I', msg_type) + data
    # 发送消息
    self.sock.sendall(msg)

2. 在客户端的`run`方法中，接收服务端发送的消息，并根据消息类型进行相应的处理，例如：

def run(self):
    while True:
        try:
            self.sock.connect(self.ADDR)
            break
        except:
            time.sleep(3)
            continue
    # 发送分辨率调整请求
    self.send_resolution_request((640, 480))
    if self.showme:
        cv2.namedWindow('You', cv2.WINDOW_NORMAL)
    print('视频客户端已连接...')
    while self.cap.isOpened():
        # 接收服务端发送的消息
        msg_type = struct.unpack('!I', self.sock.recv(4))[0]
        if msg_type == 1:  # 分辨率调整消息
            # 解析消息内容，获取分辨率参数
            resolution = pickle.loads(self.sock.recv(1024))
            # 调整视频分辨率
            self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, resolution[0])
            self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, resolution[1])
        ret, frame = self.cap.read()
        if self.showme:
            cv2.imshow('You', frame)
            if cv2.waitKey(1) & 0xFF == 27:
                self.showme = False
                cv2.destroyWindow('You')
        # 缩放
        sframe = cv2.resize(frame, (0, 0), fx=self.fx, fy=self.fx)
        data = pickle.dumps(sframe)
        zdata = zlib.compress(data, zlib.Z_BEST_COMPRESSION)
        try:
            self.sock.sendall(struct.pack("L", len(zdata)) + zdata)
        except:
            break
        for i in range(self.interval):
            self.cap.read()

在客户端的`run`方法中，首先发送分辨率调整请求，然后在循环中接收服务端发送的消息。如果收到的消息类型是分辨率调整消息，则解析消息内容，获取分辨率参数，并调用`cap.set`方法设置视频分辨率。

将#!/usr/bin/env python2.7 -- coding: UTF-8 -- import time import cv2 from PIL import Image import numpy as np from PIL import Image if name == 'main': rtsp_url = "rtsp://127.0.0.1:8554/live" cap = cv2.VideoCapture(rtsp_url) #判断摄像头是否可用 #若可用，则获取视频返回值ref和每一帧返回值frame if cap.isOpened(): ref, frame = cap.read() else: ref = False #间隔帧数 imageNum = 0 sum=0 timeF = 24 while ref: ref,frame=cap.read() sum+=1 #每隔timeF获取一张图片并保存到指定目录 #"D:/photo/"根据自己的目录修改 if (sum % timeF == 0): # 格式转变，BGRtoRGB frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # 转变成Image frame = Image.fromarray(np.uint8(frame)) frame = np.array(frame) # RGBtoBGR满足opencv显示格式 frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) imageNum = imageNum + 1 cv2.imwrite("/root/Pictures/Pictures" + str(imageNum) + '.png', frame) print("success to get frame") #1毫秒刷新一次 k = cv2.waitKey(1) #按q退出 #if k==27：则为按ESC退出 if k == ord('q'): cap.release() break和#!/usr/bin/env python2.7 coding=UTF-8 import os import sys import cv2 from pyzbar import pyzbar def main(image_folder_path, output_file_name): img_files = [f for f in os.listdir(image_folder_path) if f.endswith(('.png'))] qr_codes_found = [] print("Image files:") for img_file in img_files: print(img_file) for img_file in img_files: img_path = os.path.join(image_folder_path,img_file) img = cv2.imread(img_path) barcodes = pyzbar.decode(img) for barcode in barcodes: if barcode.type == 'QRCODE': qr_data = barcode.data.decode("utf-8") qr_codes_found.append((img_file, qr_data)) unique_qr_codes = [] for file_name, qr_content in qr_codes_found: if qr_content not in unique_qr_codes: unique_qr_codes.append(qr_content) with open(output_file_name,'w') as f: for qr_content in unique_qr_codes: f.write("{}\n".format(qr_content)) if name == "main": image_folder_path = '/root/Pictures' output_file_name = 'qr_codes_found.txt' main(image_folder_path,output_file_name)合并成一个代码

以下是将两段代码合并为一个代码的结果：

#!/usr/bin/env python2.7
# -*- coding: UTF-8 -*-

import time
import cv2
from PIL import Image
import numpy as np
from PIL import Image
import os
import sys
from pyzbar import pyzbar

def main(image_folder_path, output_file_name):
    img_files = [f for f in os.listdir(image_folder_path) if f.endswith(('.png'))]
    qr_codes_found = []

    print("Image files:")
    for img_file in img_files:
        print(img_file)

    for img_file in img_files:
        img_path = os.path.join(image_folder_path,img_file)
        img = cv2.imread(img_path)
        barcodes = pyzbar.decode(img)
        
        for barcode in barcodes:
            if barcode.type == 'QRCODE':
                qr_data = barcode.data.decode("utf-8")
                qr_codes_found.append((img_file, qr_data))
               
    unique_qr_codes = []
    for file_name, qr_content in qr_codes_found:
        if qr_content not in unique_qr_codes:
            unique_qr_codes.append(qr_content)
        
    with open(output_file_name,'w') as f:
        for qr_content in unique_qr_codes:
            f.write("{}\n".format(qr_content))

if __name__ == '__main__':
    rtsp_url = "rtsp://127.0.0.1:8554/live"
    cap = cv2.VideoCapture(rtsp_url)

    # 判断摄像头是否可用
    # 若可用，则获取视频返回值ref和每一帧返回值frame
    if cap.isOpened():
        ref, frame = cap.read()
    else:
        ref = False

    # 间隔帧数
    imageNum = 0
    sum = 0
    timeF = 24

    while ref:
        ref, frame = cap.read()
        sum += 1

        # 每隔timeF获取一张图片并保存到指定目录
        # "D:/photo/"根据自己的目录修改
        if (sum % timeF == 0):

            # 格式转变，BGRtoRGB
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            # 转变成Image
            frame = Image.fromarray(np.uint8(frame))
            frame = np.array(frame)
            # RGBtoBGR满足opencv显示格式
            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)

            imageNum = imageNum + 1
            cv2.imwrite("/root/Pictures/Pictures" + str(imageNum) + '.png', frame)

            print("success to get frame")

    # 1毫秒刷新一次
    k = cv2.waitKey(1)

    # 按q退出
    # 如果按下的是q键，则退出循环
    if k == ord('q'):
        cap.release()

    image_folder_path = '/root/Pictures'
    output_file_name = 'qr_codes_found.txt'
    main(image_folder_path,output_file_name)

这样，代码中的摄像头捕获和图像识别逻辑将在同一个程序中运行，并且在摄像头捕获每一帧图像后会进行图像识别操作。您可以根据需要对代码进行进一步的调整和优化。