opencv_extract_object.py

import numpy as np
import argparse
import glob
import cv2
import json

def auto_canny(image, sigma=0.33):
	otsu_thresh_val, ret = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY|cv2.THRESH_OTSU)

	lower = otsu_thresh_val * 0.3
	upper = otsu_thresh_val * 1
	edged = cv2.Canny(image, lower, upper)

	return edged

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--images", required=True,
	help="path to input dataset of images")
args = vars(ap.parse_args())

for imagePath in glob.glob(args["images"] + "/*.jpg"):
	image = cv2.imread(imagePath)
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	blurred = cv2.GaussianBlur(gray, (3, 3), 0)

	edged = auto_canny(gray)

	kernel = np.ones((20, 20), np.uint8)
	morphed = cv2.morphologyEx(edged, cv2.MORPH_CLOSE, kernel)

	(_, contours, _) = cv2.findContours(morphed.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
	main_contour = sorted(contours, key = cv2.contourArea, reverse = True)[0]

	peri = cv2.arcLength(main_contour, False)
	approx = cv2.approxPolyDP(main_contour, 0.0015 * peri, True)

	cv2.drawContours(image, [approx], -1, (0, 255, 0), 2)

	print(json.dumps(approx.tolist()));

	cv2.imshow("Result", image)
	cv2.waitKey(0)