img = cv2.imread(os.path.join(data_path, file))和img = cv2.imread(file)的区别
时间: 2024-03-29 11:35:27 浏览: 194
这两行代码的区别在于第一个代码使用了`os.path.join()`函数来拼接图片所在的完整路径,而第二个代码直接使用了相对路径或绝对路径来读取图片。如果你已经确定了图片所在的路径并且可以直接访问,那么两行代码的效果是一样的。但是如果图片所在的路径是动态生成的或者不确定的,那么使用第一个代码可以更加灵活地拼接路径。
相关问题
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)合并一下
您可以将两个代码合并为一个ROS节点,以便在同一个程序中运行。下面是合并后的代码:
```python
#!/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
from std_msgs.msg import String
import rospy
def capture_and_recognize():
# 初始化ROS节点
rospy.init_node('capture_and_recognize_node', anonymous=True)
# 创建图像识别结果的发布者
pub = rospy.Publisher('recognition_result', String, queue_size=10)
# 设置摄像头参数
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")
# 图像识别
img_path = "/root/Pictures/Pictures" + str(imageNum) + '.png'
img = cv2.imread(img_path)
barcodes = pyzbar.decode(img)
qr_codes_found = []
for barcode in barcodes:
if barcode.type == 'QRCODE':
qr_data = barcode.data.decode("utf-8")
qr_codes_found.append(qr_data)
unique_qr_codes = []
for qr_content in qr_codes_found:
if qr_content not in unique_qr_codes:
unique_qr_codes.append(qr_content)
# 发布图像识别结果
for qr_content in unique_qr_codes:
pub.publish(qr_content)
# 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()
break
if __name__ == '__main__':
try:
capture_and_recognize()
except rospy.ROSInterruptException:
pass
```
在这个合并的代码中,我们使用了ROS的Python库rospy来创建一个ROS节点。我们初始化了一个节点,创建了一个用于发布图像识别结果的话题,并在适当的位置发布识别结果。您可以根据需要进一步调整代码以满足您的机器人运行环境的要求。
# 加载数据集 data_dir =r"C:\Users\HP\Desktop\image\save" patients = os.listdir(data_dir) images = [] for patient in patients: patient_dir = os.path.join(data_dir, patient) imgs = os.listdir(patient_dir) for img_name in imgs: img_path = os.path.join(patient_dir, img_name) img = cv2.imread(img_path) img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_gray = cv2.resize(img_gray, (256, 256)) img_gray = img_gray / 255.0 images.append(img_gray) images = np.array(images) images = np.reshape(images, (-1, 256, 256, 1)) 如何修改这段代码使得这段代码可以读取save文件夹下面所有病人的图片
您可以使用 os.walk() 函数来遍历 save 文件夹下的所有子文件夹和文件,然后根据文件名中的信息来判断是否是病人的图片。以下是修改后的代码:
```
import os
import cv2
import numpy as np
data_dir = r"C:\Users\HP\Desktop\image\save"
images = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if "patient" in file: # 判断文件名中是否包含病人信息
img_path = os.path.join(root, file)
img = cv2.imread(img_path)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray = cv2.resize(img_gray, (256, 256))
img_gray = img_gray / 255.0
images.append(img_gray)
images = np.array(images)
images = np.reshape(images, (-1, 256, 256, 1))
```
这样可以读取 save 文件夹下所有病人的图片,不论这些图片在哪个子文件夹下。
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