Added image_align_scan.py

image_align_scan.py

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+from skimage.filters import threshold_local
+import numpy as np
+import argparse
+import cv2
+import imutils
+import os
+
+
+current_path=os.getcwd()
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", required = True)
+args = vars(ap.parse_args())
+
+
+
+def order_points(pts):
+	rect = np.zeros((4, 2), dtype = "float32")
+	s = pts.sum(axis = 1)
+	rect[0] = pts[np.argmin(s)]
+	rect[2] = pts[np.argmax(s)]
+	diff = np.diff(pts, axis = 1)
+	rect[1] = pts[np.argmin(diff)]
+	rect[3] = pts[np.argmax(diff)]
+	return rect
+
+
+
+def four_point_transform(image, pts):
+	rect = order_points(pts)
+	(tl, tr, br, bl) = rect
+	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))
+	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))
+	dst = np.array([
+		[0, 0],
+		[maxWidth - 1, 0],
+		[maxWidth - 1, maxHeight - 1],
+		[0, maxHeight - 1]], dtype = "float32")
+	M = cv2.getPerspectiveTransform(rect, dst)
+	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+	return warped
+
+
+
+
+
+image = cv2.imread(args["image"])
+ratio = image.shape[0] / 500.0
+orig = image.copy()
+image = imutils.resize(image, height = 500)
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+gray = cv2.GaussianBlur(gray, (5, 5), 0)
+edged = cv2.Canny(gray, 75, 200)
+cv2.imshow("Image", image)
+cv2.imshow("Edged", edged)
+print("STEP 1: Edge Detection")
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+
+
+
+
+cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
+cnts = imutils.grab_contours(cnts)
+cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
+for c in cnts:
+	peri = cv2.arcLength(c, True)
+	approx = cv2.approxPolyDP(c, 0.02 * peri, True)
+	if len(approx) == 4:
+		screenCnt = approx
+		break
+print("STEP 2: Find contours of paper")
+cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
+cv2.imshow("Outline", image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+
+
+
+warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
+warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
+
+# warped = cv2.adaptiveThreshold(warped, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 251, 11)
+
+
+
+T = threshold_local(warped, 11, offset = 10, method = "gaussian")
+warped = (warped > T).astype("uint8") * 255
+
+print("STEP 3: Apply perspective transform")
+cv2.imshow("Original", imutils.resize(orig, height = 650))
+cv2.imshow("Scanned", imutils.resize(warped, height = 650))
+cv2.waitKey(0)
+
+
+
+cv2.imwrite(current_path+'/Scanned.png',imutils.resize(warped, height = 650))