Extract Bild von ROI (OpenCV)

Question

Da der folgende Code (Python) ...

# 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") 

und das Bild verwendet in ...

Test Image

I ROI durchführen können und dabei sparen es. Das Problem besteht darin, dass der Code nur die erste Ziffer speichert (vielleicht wegen der "für die Rekruten" in Zeile 32). Was muss ich ändern, um alle erkannten Zeichen zu speichern (das mit der Bounding Box herum)?

Denken Sie auch an 10 des Beispielbildes. Ich muss alle in einem Ordner speichern, jeder mit dem unterschiedlichen Dateinamen (automatisch). Wie es geht?

Vielen Dank

Answer 1

Hier ist ein Code, der die Anfrage beantwortet. Die einzige Sache ist, dass es die Zeichen nicht in einer bestimmten Weise anordnet, sondern wie sie sie erkennt.

# 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")