在python中使用opencv检测低对比度图像中的regtangles以便通过tesseract读取

Question

我想要检测图像中的标签，例如使用tesseract提取文本的目的。我尝试过使用阈值检测和边缘检测的各种组合。不过，我一次只能检测到大约一半的标签。这是一些图片我一直在试图读取标签：

enter image description here

enter image description here

所有标签都具有相同的纵横比（宽度比大3.5倍高度），所以我试图找到具有相同高宽比的minAreaRect的轮廓。困难的部分是将标签交给更轻的背景。这是我到目前为止的代码：

from PIL import Image 
import pytesseract 
import numpy as np 
import argparse 
import cv2 
import os 

ap = argparse.ArgumentParser() 
ap.add_argument("-i", "--image", required=True, 
    help="path to input image to be OCR'd") 
args = vars(ap.parse_args()) 

#function to crop an image to a minAreaRect 
def crop_minAreaRect(img, rect): 
    # rotate img 
    angle = rect[2] 
    rows,cols = img.shape[0], img.shape[1] 
    M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1) 
    img_rot = cv2.warpAffine(img,M,(cols,rows)) 

    # rotate bounding box 
    rect0 = (rect[0], rect[1], 0.0) 
    box = cv2.boxPoints(rect) 
    pts = np.int0(cv2.transform(np.array([box]), M))[0] 
    pts[pts < 0] = 0 

    # crop 
    img_crop = img_rot[pts[1][1]:pts[0][1], 
         pts[1][0]:pts[2][0]] 

    return img_crop 




# load image and apply threshold 
image = cv2.imread(args["image"]) 
bw = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
#bw = cv2.threshold(bw, 210, 255, cv2.THRESH_BINARY)[1] 
bw = cv2.adaptiveThreshold(bw, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 27, 20) 
#do edge detection 
v = np.median(bw) 
sigma = 0.5 
lower = int(max(0, (1.0 - sigma) * v)) 
upper = int(min(255, (1.0 + sigma) * v)) 
bw = cv2.Canny(bw, lower, upper) 
kernel = np.ones((5,5), np.uint8) 
bw = cv2.dilate(bw,kernel,iterations=1) 

#find contours 
image2, contours, hierarchy = cv2.findContours(bw,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) 
bw = cv2.drawContours(bw,contours,0,(0,0,255),2) 
cv2.imwrite("edge.png", bw) 

#test which contours have the correct aspect ratio 
largestarea = 0.0 
passes = [] 
for contour in contours: 
    (x,y),(w,h),a = cv2.minAreaRect(contour) 
    if h > 20 and w > 20: 
     if h > w: 
      maxdim = h 
      mindim = w 
     else: 
      maxdim = w 
      mindim = h 
     ratio = maxdim/mindim 
     print("ratio: {}".format(ratio)) 
     if (ratio > 3.4 and ratio < 3.6): 
      passes.append(contour) 
if not passes: 
    print "no passes" 
    exit() 

passboxes = [] 
i = 1 

#crop out each label and attemp to extract text 
for ps in passes: 
    rect = cv2.minAreaRect(ps) 
    bw = crop_minAreaRect(image, rect) 
    cv2.imwrite("{}.png".format(i), bw) 
    i += 1 
    h, w = bw.shape[:2] 
    print str(h) + "x" + str(w) 
    if w and h: 
     bw = cv2.cvtColor(bw, cv2.COLOR_BGR2GRAY) 
     bw = cv2.threshold(bw, 50, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] 
     cv2.imwrite("output.png", bw) 
     im = Image.open("output.png") 
     w, h = im.size 
     print "W:{} H:{}".format(w,h) 
     if h > w: 
      print ("rotating") 
      im.rotate(90) 
      im.save("output.png") 
     print pytesseract.image_to_string(Image.open("output.png")) 
     im.rotate(180) 
     im.save("output.png") 
     print pytesseract.image_to_string(Image.open("output.png")) 
     box = cv2.boxPoints(cv2.minAreaRect(ps)) 
     passboxes.append(np.int0(box)) 
     im.close() 

cnts = cv2.drawContours(image,passboxes,0,(0,0,255),2) 
cnts = cv2.drawContours(cnts,contours,-1,(255,255,0),2) 
cnts = cv2.drawContours(cnts, passes, -1, (0,255,0), 3) 
cv2.imwrite("output2.png", image) 

我相信我的问题可能是阈值的参数。或者我可能会因此而复杂化。

Answer 1

只有带有“A-08337”等的白色标签？以下检测所有的人在这两个图像：

import numpy as np 
import cv2 

img = cv2.imread('labels.jpg') 

#downscale the image because Canny tends to work better on smaller images 
w, h, c = img.shape 
resize_coeff = 0.25 
img = cv2.resize(img, (int(resize_coeff*h), int(resize_coeff*w))) 

#find edges, then contours 
canny = cv2.Canny(img, 100, 200) 
_, contours, _ = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 

#draw the contours, do morphological close operation 
#to close possible small gaps, then find contours again on the result 
w, h, c = img.shape 
blank = np.zeros((w, h)).astype(np.uint8) 
cv2.drawContours(blank, contours, -1, 1, 1) 
blank = cv2.morphologyEx(blank, cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8)) 
_, contours, _ = cv2.findContours(blank, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) 

#keep only contours of more or less correct area and perimeter 
contours = [c for c in contours if 800 < cv2.contourArea(c) < 1600] 
contours = [c for c in contours if cv2.arcLength(c, True) < 200] 
cv2.drawContours(img, contours, -1, (0, 0, 255), 1) 

cv2.imwrite("contours.png", img) 

可能与一些额外的凸检查是否能摆脱“逐字”轮廓等（例如，只保留轮廓与他们的区域之间接近零差它们的凸包区域）。