learnopencv/Automatic-Document-Scanner/grabcut-scan.py

import cv2
import numpy as np
import glob
import time
import csv


def order_points(pts):
    """Rearrange coordinates to order:
       top-left, top-right, bottom-right, bottom-left"""
    rect = np.zeros((4, 2), dtype='float32')
    pts = np.array(pts)
    s = pts.sum(axis=1)
    # Top-left point will have the smallest sum.
    rect[0] = pts[np.argmin(s)]
    # Bottom-right point will have the largest sum.
    rect[2] = pts[np.argmax(s)]

    diff = np.diff(pts, axis=1)
    # Top-right point will have the smallest difference.
    rect[1] = pts[np.argmin(diff)]
    # Bottom-left will have the largest difference.
    rect[3] = pts[np.argmax(diff)]
    # return the ordered coordinates
    return rect.astype('int').tolist()


def scan(img):
    # Resize image to workable size
    dim_limit = 1080
    max_dim = max(img.shape)
    if max_dim > dim_limit:
        resize_scale = dim_limit / max_dim
        img = cv2.resize(img, None, fx=resize_scale, fy=resize_scale)

    # Create a copy of resized original image for later use
    orig_img = img.copy()
    # cv2.imshow("original_resized", orig_img)

    # Repeated Closing operation to remove text from the document.
    kernel = np.ones((5, 5), np.uint8)
    img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=3)
    # cv2.imshow("morphologyEX", img)

    # GrabCut
    mask = np.zeros(img.shape[:2], np.uint8)
    bgdModel = np.zeros((1, 65), np.float64)
    fgdModel = np.zeros((1, 65), np.float64)
    rect = (20, 20, img.shape[1] - 20, img.shape[0] - 20)
    cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
    mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
    img = img * mask2[:, :, np.newaxis]
    # cv2.imshow("grabcut", img)

    # Convert to grayscale.
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (11, 11), 0)
    # cv2.imshow("gray_blurred", gray)

    # Edge Detection.
    canny = cv2.Canny(gray, 0, 200)
    canny = cv2.dilate(canny, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))
    # cv2.imshow("canny_dilate", canny)

    # Finding contours for the detected edges.
    contours, hierarchy = cv2.findContours(canny, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
    # Keeping only the largest detected contour.
    page = sorted(contours, key=cv2.contourArea, reverse=True)[:5]

    # Detecting Edges through Contour approximation.
    # Loop over the contours.
    if len(page) == 0:
        return orig_img
    for c in page:
        # Approximate the contour.
        epsilon = 0.02 * cv2.arcLength(c, True)
        corners = cv2.approxPolyDP(c, epsilon, True)
        # If our approximated contour has four points.
        if len(corners) == 4:
            break

    # Sorting the corners and converting them to desired shape.
    corners = sorted(np.concatenate(corners).tolist())
    # For 4 corner points being detected.
    # Rearranging the order of the corner points.
    corners = order_points(corners)
    (tl, tr, br, bl) = corners

    # Draw points
    points_img = cv2.cvtColor(canny, cv2.COLOR_GRAY2BGR)
    point_count = 0
    for corner in corners:
        cv2.circle(points_img, corner, 3, (255, 0, 0), -1)
        point_count += 1
        cv2.putText(points_img, str(point_count), corner, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    # cv2.imshow("points", points_img)

    # Finding the maximum width.
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    maxWidth = max(int(widthA), int(widthB))

    # Finding the maximum height.
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    maxHeight = max(int(heightA), int(heightB))

    # Final destination co-ordinates.
    destination_corners = [[0, 0], [maxWidth, 0], [maxWidth, maxHeight], [0, maxHeight]]

    # Getting the homography.
    M = cv2.getPerspectiveTransform(np.float32(corners), np.float32(destination_corners))
    # Perspective transform using homography.
    final = cv2.warpPerspective(orig_img, M, (maxWidth, maxHeight), flags=cv2.INTER_LINEAR)

    return final


runtime = []
for img_path in glob.glob('inputs/*.jpg'):
    try:
        img = cv2.imread(img_path)
        print(img_path)

        t1 = time.time()

        scanned_img = scan(img)

        t2 = time.time()

        runtime.append({'image': img_path, 'time': t2 - t1})

        # cv2.imshow("scanner", scanned_img)
        cv2.imwrite('grabcutop/' + img_path.split('/')[-1], scanned_img)
        print("scanned")

        key = cv2.waitKey(0)
        if key == 27:
            break
    except:
        print('fail')

csv_columns = ['image', 'time']
with open('time.csv', 'w') as f:
    writer = csv.DictWriter(f, fieldnames=csv_columns)
    writer.writeheader()
    for data in runtime:
        writer.writerow(data)

cv2.destroyAllWindows()