Crop all characters from an image-CodePudding

I put together some code to extract all characters from an image. I sort the characters from left to right and I try to crop each character into a separate image. Not all characters are properly cropped, some of them end up having size zero.

The only characters that do not have one dimension zero are BCDEF. Here is an image of the output.

import cv2
import numpy as np

def crop_minAreaRect(img, rect):
# https://stackoverflow.com/questions/37177811/crop-rectangle-returned-by-minarearect-opencv-python
    # rotate img
    center = rect[0]
    size = rect[1]
    print("size[0]: "   str(int(size[0]))   ", size[1]: "   str(int(size[1])))
    angle = rect[2]
    print("angle: "   str(angle))
    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], angle) 
    box = cv2.boxPoints(rect0)
    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]]

    w, h = img_crop.shape[0], img_crop.shape[1]
    print("w_cropped: "   str(w)   ", h_cropped: "   str(h))
    return img_crop

def sort_contours(cnts, method="left-to-right"):
# from https://pyimagesearch.com/2015/04/20/sorting-contours-using-python-and-opencv/    
    reverse = False
    i = 0
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),   key=lambda b:b[1][i], reverse=reverse))
    return (cnts, boundingBoxes)    
    
im_name = 'letters.png'
im = cv2.imread(im_name)
im_copy = im.copy()

imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

#cv2.drawContours(im_copy, contours, -1, (0,255,0), 2)
#cv2.imshow("contours", im_copy)

print("num contours: "   str(len(contours)))
i = 0

sorted_cnts, bounding_boxes = sort_contours(contours, method="left-to-right")

for cnt in sorted_cnts:
  size = cv2.contourArea(cnt)
  x,y,w,h = cv2.boundingRect(cnt)
  rect = cv2.minAreaRect(cnt)
#  print(str(rect))
#  if rect[1][0] > 0 and rect[1][1]>0:
  im_cropped = crop_minAreaRect(im, rect)
    
  h,w = im_cropped.shape[0], im_cropped.shape[1]
  if w > h:
    im_cropped = cv2.rotate(im_cropped, cv2.ROTATE_90_CLOCKWISE)  
    
  print("w: "   str(w)   ", h: "   str(h))
  if w>0 and h>0:
    cv2.imshow("cropped"   str(i), im_cropped)
  i  = 1
#    cv2.waitKey(0)

cv2.waitKey(0)

CodePudding user response：

There appears to be an error in your crop_minAreaRect function. I haven't debugged your code any further than the return of crop_minAreaRect, so the letters may or may not be correctly rotated according following your approach, but this change fixes the underlying problem.
The proposed function is taken from the following question and modified: How to straighten a rotated rectangle area of an image using OpenCV in Python?

import cv2
import numpy as np


def subimage(image, center, theta, width, height):
    '''
   Rotates OpenCV image around center with angle theta (in deg)
   then crops the image according to width and height.
   '''
    width = int(width)
    height = int(height)
    # Uncomment for theta in radians
    # theta *= 180/np.pi

    shape = (image.shape[1], image.shape[0])  # cv2.warpAffine expects shape in (length, height)

    matrix = cv2.getRotationMatrix2D(center=center, angle=theta, scale=1)
    image = cv2.warpAffine(src=image, M=matrix, dsize=shape)

    x = int(center[0] - width / 2)
    y = int(center[1] - height / 2)

    image = image[y:y   height, x:x   width]

    return image


def sort_contours(cnts, method="left-to-right"):
    # from https://pyimagesearch.com/2015/04/20/sorting-contours-using-python-and-opencv/
    reverse = False
    i = 0
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), key=lambda b: b[1][i], reverse=reverse))
    return (cnts, boundingBoxes)


im_name = 'letters.png'
im = cv2.imread(im_name)
im_copy = im.copy()

imgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cv2.drawContours(im_copy, contours, -1, (0, 255, 0), 2)
cv2.imshow("contours", im_copy)

# print("num contours: "   str(len(contours)))
i = 0

sorted_cnts, bounding_boxes = sort_contours(contours, method="left-to-right")

for cnt in sorted_cnts:
    size = cv2.contourArea(cnt)
    x, y, w, h = cv2.boundingRect(cnt)
    rect = cv2.minAreaRect(cnt)


    im_cropped = subimage(im, center=rect[0], theta=rect[2], width=rect[1][0], height=rect[1][0])

    h, w = im_cropped.shape[0], im_cropped.shape[1]
    if w > h:
        im_cropped = cv2.rotate(im_cropped, cv2.ROTATE_90_CLOCKWISE)

    # print("w: "   str(w)   ", h: "   str(h))
    if w > 0 and h > 0:
        cv2.imshow("cropped"   str(i), im_cropped)
    i  = 1
#    cv2.waitKey(0)

cv2.waitKey(0)