TUTO_edge_detection.py

import cv2
import numpy as np

"""
REFER: https://hub.packtpub.com/opencv-detecting-edges-lines-shapes/
2018-06-30 Yonv1943
2018-07-01 comment to test.png
2018-07-01 gray in threshold, hierarchy
2018-07-01 draw_approx_hull_polygon() no [for loop]
2018-11-24 
"""


def draw_contours(img, cnts):  # conts = contours
    img = np.copy(img)
    img = cv2.drawContours(img, cnts, -1, (0, 255, 0), 2)
    return img


def draw_min_rect_circle(img, cnts):  # conts = contours
    img = np.copy(img)

    for cnt in cnts:
        x, y, w, h = cv2.boundingRect(cnt)
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)  # blue

        min_rect = cv2.minAreaRect(cnt)  # min_area_rectangle
        min_rect = np.int0(cv2.boxPoints(min_rect))
        cv2.drawContours(img, [min_rect], 0, (0, 255, 0), 2)  # green

        (x, y), radius = cv2.minEnclosingCircle(cnt)
        center, radius = (int(x), int(y)), int(radius)  # center and radius of minimum enclosing circle
        img = cv2.circle(img, center, radius, (0, 0, 255), 2)  # red
    return img


def draw_approx_hull_polygon(img, cnts):
    # img = np.copy(img)
    img = np.zeros(img.shape, dtype=np.uint8)

    cv2.drawContours(img, cnts, -1, (255, 0, 0), 2)  # blue

    min_side_len = img.shape[0] / 32  # 多边形边长的最小值 the minimum side length of polygon
    min_poly_len = img.shape[0] / 16  # 多边形周长的最小值 the minimum round length of polygon
    min_side_num = 3  # 多边形边数的最小值
    approxs = [cv2.approxPolyDP(cnt, min_side_len, True) for cnt in cnts]  # 以最小边长为限制画出多边形
    approxs = [approx for approx in approxs if cv2.arcLength(approx, True) > min_poly_len]  # 筛选出周长大于 min_poly_len 的多边形
    approxs = [approx for approx in approxs if len(approx) > min_side_num]  # 筛选出边长数大于 min_side_num 的多边形
    # Above codes are written separately for the convenience of presentation.
    cv2.polylines(img, approxs, True, (0, 255, 0), 2)  # green

    hulls = [cv2.convexHull(cnt) for cnt in cnts]
    cv2.polylines(img, hulls, True, (0, 0, 255), 2)  # red

    # for cnt in cnts:
    #     cv2.drawContours(img, [cnt, ], -1, (255, 0, 0), 2)  # blue
    #
    #     epsilon = 0.02 * cv2.arcLength(cnt, True)
    #     approx = cv2.approxPolyDP(cnt, epsilon, True)
    #     cv2.polylines(img, [approx, ], True, (0, 255, 0), 2)  # green
    #
    #     hull = cv2.convexHull(cnt)
    #     cv2.polylines(img, [hull, ], True, (0, 0, 255), 2)  # red
    return img


def run():
    image = cv2.imread('Demo/test_edge_detection.jpg')  # a black objects on white image is better

    # gray = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY)
    # ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
    thresh = cv2.Canny(image, 128, 256)

    thresh, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    # print(hierarchy, ":hierarchy")
    """
    [[[-1 -1 -1 -1]]] :hierarchy  # cv2.Canny()
    
    [[[ 1 -1 -1 -1]
      [ 2  0 -1 -1]
      [ 3  1 -1 -1]
      [-1  2 -1 -1]]] :hierarchy  # cv2.threshold()
    """

    imgs = [
        image, thresh,
        draw_min_rect_circle(image, contours),
        draw_approx_hull_polygon(image, contours),
    ]

    for img in imgs:
        # cv2.imwrite("%s.jpg" % id(img), img)
        cv2.imshow("contours", img)
        cv2.waitKey(1943)


if __name__ == '__main__':
    run()
pass