这时，自动化的 Optical Character Recognition（OCR，光学字符识别）技术就能派上用场。
options = {"detect_direction":"true"} # 调用通用文字识别接口并把结果赋值给result result = client.basicAccurate(image, options) # 输出result # print(result) ending = result["words_result"] # print(ending) for word in ending: # print(w...
result={}try:result=client.basicGeneral(image,options)ifresult.get("error_msg"):print(result['error_msg'])contents.SetValue(result['error_msg'])else:resultword=result['words_result']num=result['words_result_num']OCRtext=[]foriinrange(0,num):print(resultword[i]['words'])#由于返回的信息...
readtext函数是将detect和recognize方法相结合:先利用 detect 函数识别图像中文本框的位置坐标,将坐标列表输入recognize进行识别,最终返回每个文本信息及位置坐标,函数框架如下: import easyocr reader = easyocr.Reader(['ch_sim','en'],gpu=False,model_storage_directory='./model') result = reader.readtext...
img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) Apply adaptive thresholding to obtain binary image _, img = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) Remove noise from the image using GaussianBlur filter img = cv2.GaussianBlur(img,(5,5),0) Detect edges in the image...
faces=face_cascade.detectMultiScale(gray,1.3,5) faces为在img图像中检测到的人脸,然后利用cv2.rectangle在人脸一圈画个矩形。并把含有人脸的区域储存进入data文件夹 注意这里写入时,每个图片的标签时Total_face_num即当前共有多少个可识别用户(在录入之前加一),亦即当前用户的编号 ...
image 表示图片; horizontal_list、free_list 分别表示矩形文本框列表,是函数 detect 的两个输出列表 使用方法如下 import easyocr from PIL import Image,ImageDraw reader = easyocr.Reader(['ch_sim','en'],gpu=False,model_storage_directory='./model') ...
readtext函数是将detect和recognize方法相结合:先利用 detect 函数识别图像中文本框的位置坐标,将坐标列表输入recognize进行识别,最终返回每个文本信息及位置坐标,函数框架如下: import easyocr reader = easyocr.Reader(['ch_sim','en'],gpu=False,model_storage_directory='./model') ...
detect=ImgText_CN(img0, '不合格', 10, 10, textColor=(255, 0, 0), textSize=30) cv2.imshow("Detect_" +str(i),detect) cv2.waitKey(0) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. ...
image_in_gray_scale = cv2.cvtColor(group_of_people_image,cv2.COLOR_BGR2GRAY)faces = frontal_face_classifier.detectMultiScale(image=image_in_gray_scale,scaleFactor=1.3, minNeighbors=6)for (x_axis, y_axis, weight,height) in faces:cv2.rectangle(group_of_people_image,(x_axis, y_axis), ...