Given the following 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")
and the image used into...
I can perform ROI and save it. The problem is that the code saves only the first digit (maybe because of the "for rects" at line 32). What I have to modify to save all the chars recognized (that with the bounding box around)?
Also, think about 10 of the example image. I have to save all of them in a folder, each with the different filename (automatically). How to do it?
Thank you
Here is a code that answer the request. The only thing is that it doesn't order the chars in a specific way but how it recognize them.
# 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")