ma=re.findall(pa,data)# findall 方法找到data中所有的符合pa的对象,添加到re中并返回 #print(ma)# 将ma中图片网址依次提取出来 i=0forimageinma:i+=1image=requests.get(image).contentprint(str(i)+'.jpg 正在保存。。。')withopen('../imgs/'+str(i)+'.jpg','wb')asf:# 注意打开的是就jpg...
from PIL import Image def find_images_in_folder(folder_path, extensions=['.jpg', '.jpeg', '.png', '.gif']): """遍历文件夹并找到所有指定扩展名的图片文件""" images = [] for root, dirs, files in os.walk(folder_path): for file in files: if file.lower().endswith(extensions): ...
im=Image.open("E:\mywife.jpg")print(im.palette) 易知,返回值为空,none 对图像进行convert操作,转换成“P”模式 代码语言:javascript 复制 @zhangzijufromPILimportImage im=Image.open("E:\mywife.jpg")new_im=im.convert('P')print(new_im.mode)print(new_im.palette) 则返回值为ImagePalette类的实...
确认登录后,会获取到一个重定向URL,可以通过以下代码获取: pythonredirect_uri =''while redirect_uri =='': response = requests.get(url) if 'window.redirect_uri' in response.text: redirect_uri = re.findall('window.redirect_uri="(.*?)"', response.text)[0] 3.获取登录凭证 获取重定向URL后,...
find = 0 start_i = 0 start_j = 0 for i in range(h): for j in range(w): if bin_img[i][j] == 0: find = 1 start_i = i start_j = j break return find,start_i,start_j def trace_contour(bin_img,find,start_i,start_j): ...
使用cv2.imread 函数从磁盘加载图片,然后通过 find_marker 函数得到图片中目标物体的坐标和长宽信息,最后根据相似三角形计算出相机的焦距。 现在有了相机的焦距,就可以计算目标物体到相机的距离了。 # loop over the images for imagePath in sorted(paths.list_images("images")): ...
if re.match('Image Make', tag): Device['品牌信息'] = str(value) if re.match('GPS GPSLatitudeRef', tag): GPS['GPSLatitudeRef'] = str(value) elif re.match('GPS GPSLatitude', tag): deg, min, sec = [x.replace(' ', '') for x in str(value)[1:-1].split(',')] # 先用...
# must import if working with opencv in python import numpy as np import cv2 # removes pixels in image that are between the range of # [lower_val,upper_val] def remove_gray(img,lower_val,upper_val): hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) ...
imgTags = soup.findAll('img')returnimgTagsdefdownloadImage(imgTag):#根据标签从该网页下载图片try:print'[+] Downloading image...'imgSrc = imgTag['src'] imgContent = urllib2.urlopen(imgSrc).read() imgName = basename(urlsplit(imgSrc)[2]) ...
if__name__=='__main__':in_img = r'C:\test\images\image01.jpg' add_border(in_img, output_image=r'C:\test\images\image01_border.jpg', border=100) add_border()函数接受3个参数:input_image是想要添加边框的图像;output_image是应用了边框后的图像;...