scan.py

########################################
# FOR USAGE: RUN BELOW COMMAND         #
# python scan.py -i images/m1.jpg #
########################################
#From other file to make a rectangle after finding four points
from pyimagesearch.transform import four_point_transform

import numpy as np
import argparse
import cv2
import imutils

### 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())

#loading image
image = cv2.imread(args["image"])

# Compute the ratio of the old height to the new height, clone it, 
# and resize it easier for compute and viewing
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)

# Gaussian Blurring to remove high frequency noise helping in
# Contour Detection 
gray = cv2.GaussianBlur(gray, (5, 5), 0)
# Canny Edge Detection
edged = cv2.Canny(gray, 75, 200)


print("STEP 1: Edge Detection")
# cv2.imshow("Image", image)
cv2.imshow("Edged", edged)

# finding 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)

## What are Contours ?
## Contours can be explained simply as a curve joining all the continuous
## points (along the boundary), having same color or intensity. 
## The contours are a useful tool for shape analysis and object detection 
## and recognition.

# Handling due to different version of OpenCV
cnts = cnts[0] if imutils.is_cv2() else cnts[1]

# Taking only the top 5 contours by Area
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

### Heuristic & Assumption

# A document scanner simply scans in a piece of paper.
# A piece of paper is assumed to be a rectangle.
# And a rectangle has four edges.
# Therefore use a heuristic like : we’ll assume that the largest
# contour in the image with exactly four points is our piece of paper to 
# be scanned.

# looping over the contours
for c in cnts:
	### Approximating the contour

	#Calculates a contour perimeter or a curve length
	peri = cv2.arcLength(c, True)
	approx = cv2.approxPolyDP(c, 0.01 * peri, True)#0.02

	# if our approximated contour has four points, then we
	# can assume that we have found our screen
	screenCnt = approx
	if len(approx) == 4:
		screenCnt = approx
		break
	
# show the contour (outline) 
print("STEP 2: Finding Boundary")

cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Boundary", image)

cv2.waitKey(0)
cv2.destroyAllWindows()