Crop exactly document paper from image

Rajat Jog :

I am using the following code to crop image currently

def crop_image(image):
image = cv2.imread(image)
original_img = image.copy()
hsv_img = convert_hsv(image)

lower_blue = np.array([0, 0, 120])
upper_blue = np.array([180, 38, 255])

masked_image = mask_img(hsv_img, lower_blue, upper_blue)
result = cv2.bitwise_and(image, image, mask=masked_image)
contours = cv2.findContours(masked_image.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
cv2.drawContours(masked_image, contours, -1, (0, 255, 0), 3)
max_area_contour = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(max_area_contour)
cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 3)
cont_filename = generate_contours_filename()
cv2.imwrite(cont_filename, np.hstack([image, result]))
logger.info('Successfuly saved file : %s' % cont_filename)
img = image[y:y+h, x:x+w]
filename = generate_filename()
cv2.imwrite(filename, img)
logger.info('Successfully saved cropped file : %s' % filename)
return img, filename

Following are theimages before and after: This is original image

This is resulting image

I need image that crops paper part only

Thanks in advance

fmw42 :

Here is one way to do that in Python/Opencv.

Read the input
Convert to grayscale
Threshold
Apply morphology to clean it of small regions
Get contours and filter to keep the largest one
Get the bounding box
Draw the largest contour filled on a black background as a mask
Apply the mask to blacken out the background of the paper
Use the bounding box to crop the masked input
Save the results

Input:

import cv2
import numpy as np

# read image as grayscale
img = cv2.imread('paper.jpg')

# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# threshold
thresh = cv2.threshold(gray, 190, 255, cv2.THRESH_BINARY)[1]\

# apply morphology
kernel = np.ones((7,7), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
kernel = np.ones((9,9), np.uint8)
morph = cv2.morphologyEx(morph, cv2.MORPH_ERODE, kernel)

# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = contours[0] if len(contours) == 2 else contours[1]
area_thresh = 0
for c in contours:
    area = cv2.contourArea(c)
    if area > area_thresh:
        area_thresh = area
        big_contour = c


# get bounding box
x,y,w,h = cv2.boundingRect(big_contour)

# draw filled contour on black background
mask = np.zeros_like(gray)
mask = cv2.merge([mask,mask,mask])
cv2.drawContours(mask, [big_contour], -1, (255,255,255), cv2.FILLED)

# apply mask to input
result1 = img.copy()
result1 = cv2.bitwise_and(result1, mask)

# crop result
result2 = result1[y:y+h, x:x+w]

# view result
cv2.imshow("threshold", thresh)
cv2.imshow("morph", morph)
cv2.imshow("mask", mask)
cv2.imshow("result1", result1)
cv2.imshow("result2", result2)
cv2.waitKey(0)
cv2.destroyAllWindows()

# save result
cv2.imwrite("paper_thresh.jpg", thresh)
cv2.imwrite("paper_morph.jpg", morph)
cv2.imwrite("paper_mask.jpg", mask)
cv2.imwrite("paper_result1.jpg", result1)
cv2.imwrite("paper_result2.jpg", result2)

Thresholded image: