提取图像

Question

考虑下面的代码（蟒蛇）...

# Import the modules 
import cv2 
from sklearn.externals import joblib 
from skimage.feature import hog 
import numpy as np 
from scipy import ndimage 
import PIL 
from PIL import Image 

# Load the classifier 
clf = joblib.load("digits_cls.pkl") 

# Read the input image 
im = cv2.imread("C:\\Users\\Wkgrp\\Desktop\\test.jpg") 

# Convert to grayscale and apply Gaussian filtering 
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) 
im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0) 

# Threshold the image 
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV) 

# Find contours in the image 
image, ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 

# Get rectangles contains each contour 
rects = [cv2.boundingRect(ctr) for ctr in ctrs] 


# For each rectangular region, calculate HOG features and predict 
# the digit using Linear SVM. 
for rect in rects: 
    # Draw the rectangles 
    cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3) 
    # Make the rectangular region around the digit 
    leng = int(rect[3] * 1.6) 
    pt1 = int(rect[1] + rect[3] // 2 - leng // 2) 
    pt2 = int(rect[0] + rect[2] // 2 - leng // 2) 
    roi = im_th[pt1:pt1+leng, pt2:pt2+leng] 
    # Resize the image 
    roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA) 
    roi = cv2.dilate(roi, (3, 3)) 

    # Calculate the HOG features - Number Recognition (Not to print...) 
    #roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False) 
    #nbr = clf.predict(np.array([roi_hog_fd], 'float64')) 
    #cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3) 


#cv2.imshow("Resulting Image with Rectangular ROIs", im) 
#cv2.waitKey() 
#cv2.imwrite("C:\\Users\\Wkgrp\\Desktop\\crop\\img_with_ROI.jpg",im) 
#cv2.imwrite("C:\\Users\\Wkgrp\\Desktop\\crop\\img_threshold.jpg",im_th) 
cv2.imwrite("C:\\Users\\Wkgrp\\Desktop\\crop\\.jpg",roi) 

print("NO ERRORS") 

和使用到的图片

Test Image

我可以执行的投资回报率和保存它。问题是代码只保存第一位数（可能是因为第32行的“rects”）。 我必须修改以保存所有可识别的字符（围绕边界框）？

另外，请考虑10个示例图像。我必须将它们全部保存在一个文件夹中，每个文件夹都有不同的文件名（自动）。怎么做？

谢谢

Answer 1

这里是回答请求的代码。 唯一的一点是它不会以特定的方式排列字符，而是如何识别它们。

# Import the modules 
import cv2 
from sklearn.externals import joblib 
from skimage.feature import hog 
import numpy as np 
from scipy import ndimage 
import PIL 
from PIL import Image 

# Load the classifier 
clf = joblib.load("digits_cls.pkl") 

# Read the input image 
im = cv2.imread("C:\\Users\\Bob\\Desktop\\causale.jpg") 

# Convert to grayscale and apply Gaussian filtering 
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) 
im_gray = cv2.GaussianBlur(im_gray, (5, 5), 0) 

# Threshold the image 
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV) 

# Find contours in the image 
image, ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 

# Get rectangles contains each contour 
rects = [cv2.boundingRect(ctr) for ctr in ctrs] 

idx =0 

for ctr in ctrs: 
    idx += 1 
    x,y,w,h = cv2.boundingRect(ctr) 
    roi=im[y:y+h,x:x+w] 
    cv2.imwrite('C:\\Users\\Bob\\Desktop\\crop\\' + str(idx) + '.jpg', roi) 
    #cv2.rectangle(im,(x,y),(x+w,y+h),(200,0,0),2) 
    #cv2.imshow('img',roi) 
    #cv2.waitKey(0) 

''' 
# For each rectangular region, calculate HOG features and predict 
# the digit using Linear SVM. 
for rect in rects: 
    # Draw the rectangles 
    cv2.rectangle(im, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3) 
    # Make the rectangular region around the digit 
    leng = int(rect[3] * 1.6) 
    pt1 = int(rect[1] + rect[3] // 2 - leng // 2) 
    pt2 = int(rect[0] + rect[2] // 2 - leng // 2) 
    roi = im_th[pt1:pt1+leng, pt2:pt2+leng] 
    # Resize the image 
    roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA) 
    roi = cv2.dilate(roi, (3, 3)) 

''' 

    # Calculate the HOG features - Number Recognition (Not to print...) 
    #roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualise=False) 
    #nbr = clf.predict(np.array([roi_hog_fd], 'float64')) 
    #cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3) 


#cv2.imshow("Resulting Image with Rectangular ROIs", im) 
#cv2.waitKey() 
#cv2.imwrite("C:\\Users\\Bob\\Desktop\\crop\\img_with_ROI.jpg",im) 
#cv2.imwrite("C:\\Users\\Bob\\Desktop\\crop\\img_threshold.jpg",im_th) 
#cv2.imwrite("C:\\Users\\Bob\\Desktop\\crop\\.jpg",roi) 

print("NO ERRORS")