orientation1.py

# from pyimagesearch.transform import four_point_transform

import numpy as np
from skimage.filters import threshold_local
import argparse
import imutils
import cv2

def order_points(pts):
	# initialzie a list of coordinates that will be ordered
	# such that the first entry in the list is the top-left,
	# the second entry is the top-right, the third is the
	# bottom-right, and the fourth is the bottom-left
	rect = np.zeros((4, 2), dtype = "float32")
 
	# the top-left point will have the smallest sum, whereas
	# the bottom-right point will have the largest sum
	s = pts.sum(axis = 1)
	rect[0] = pts[np.argmin(s)]
	rect[2] = pts[np.argmax(s)]
 
	# now, compute the difference between the points, the
	# top-right point will have the smallest difference,
	# whereas the bottom-left will have the largest difference
	diff = np.diff(pts, axis = 1)
	rect[1] = pts[np.argmin(diff)]
	rect[3] = pts[np.argmax(diff)]
 
	# return the ordered coordinates
	return rect

def four_point_transform(image, pts):
	# obtain a consistent order of the points and unpack them
	# individually
	rect = order_points(pts)
	(tl, tr, br, bl) = rect
 
	# compute the width of the new image, which will be the
	# maximum distance between bottom-right and bottom-left
	# x-coordiates or the top-right and top-left x-coordinates
	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))
 
	# compute the height of the new image, which will be the
	# maximum distance between the top-right and bottom-right
	# y-coordinates or the top-left and bottom-left y-coordinates
	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))
 
	# now that we have the dimensions of the new image, construct
	# the set of destination points to obtain a "birds eye view",
	# (i.e. top-down view) of the image, again specifying points
	# in the top-left, top-right, bottom-right, and bottom-left
	# order
	dst = np.array([
		[0, 0],
		[maxWidth - 1, 0],
		[maxWidth - 1, maxHeight - 1],
		[0, maxHeight - 1]], dtype = "float32")
 
	# compute the perspective transform matrix and then apply it
	M = cv2.getPerspectiveTransform(rect, dst)
	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
 
	# return the warped image
	return warped



# construct the argument parser and parse the arguments
#ap = argparse.ArgumentParser()
#ap.add_argument("-i", "--image", required = True,
#	help = "Path to the image to be scanned")
#args = vars(ap.parse_args())

# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
def angle(image):
	ratio = image.shape[0] / 500.0
	orig = image.copy()
	image = imutils.resize(image, height = 500)
	 

	# convert the image to grayscale, blur it, and find edges
	# in the image
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	gray = cv2.GaussianBlur(gray, (5, 5), 0)
	edged = cv2.Canny(gray, 75, 200)
	 
	# show the original image and the edge detected image
	print("STEP 1: Edge Detection")
	# cv2.imshow("Image", image)
	# cv2.imshow("Edged", edged)
	# cv2.waitKey(0)
	# cv2.imwrite('edged.jpg', edged) 
	# cv2.destroyAllWindo/ws()


	# Building a Document Scanner App using Python, OpenCV, and Computer VisionPython
	# find the contours in the edged image, keeping only the
	# largest ones, and initialize the screen contour
	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]

	# loop over the contours
	for c in cnts:
		# approximate the contour
		peri = cv2.arcLength(c, True)
		approx = cv2.approxPolyDP(c, 0.02 * peri, True)

		# if our approximated contour has four points, then we
		# can assume that we have found our screen
		if len(approx) == 4:
			screenCnt = approx
			break

	# show the contour (outline) of the piece of paper
	print("STEP 2: Find contours of paper")
	cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
	# cv2.imshow("Outline", image)
	# cv2.waitKey(0)
	# # cv2.imwrite('outline.jpg', image) 
	# cv2.destroyAllWindows()

	# find the contours in the edged image, keeping only the
	# largest ones, and initialize the screen contour
	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]
	 
	# loop over the contours
	for c in cnts:
		# approximate the contour
		peri = cv2.arcLength(c, True)
		approx = cv2.approxPolyDP(c, 0.02 * peri, True)
	 
		# if our approximated contour has four points, then we
		# can assume that we have found our screen
		if len(approx) == 4:
			screenCnt = approx
			break
	 
	# show the contour (outline) of the piece of paper

	# apply the four point transform to obtain a top-down
	# view of the original image
	warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
	 
	# convert the warped image to grayscale, then threshold it
	# to give it that 'black and white' paper effect
	warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
	T = threshold_local(warped, 11, offset = 10, method = "gaussian")
	warped = (warped > T).astype("uint8") * 255
	 
	# show the original and scanned images
	print("STEP 3: Apply perspective transform")
	cv2.imshow("Original", imutils.resize(orig, height = 650))
	cv2.imshow("Scanned", imutils.resize(warped, height = 650))
	qwe=imutils.resize(warped, height = 650)
	dimensions=qwe.shape
	print(dimensions)
	# cv2.imwrite("Scanned.jpg",qwe)
	# cv2.waitKey(0)
	return qwe