l5

import cv2
import numpy as np
from matplotlib import pyplot as plt


# template (pattern) matching
img = cv2.imread('test.jpg', 1)
temp = cv2.imread('template.jpg', 1)

w = temp.shape[1]
h = temp.shape[0]

# check docs for rest of template matching methods
method = cv2.TM_CCORR_NORMED

res = cv2.matchTemplate(img, temp, method)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
    top_left = min_loc
else:
    top_left = max_loc
bottom_right = (top_left[0] + w, top_left[1] + h)

cv2.rectangle(img,top_left, bottom_right, (0, 0, 255), 2)

cv2.imshow('res', img)
cv2.imshow('template', temp)
cv2.waitKey()



# contour matching
img = cv2.imread('bottles.jpg', 0)
temp = cv2.imread('bottle.jpg', 0)

kernel = np.ones((5,5), np.uint8)

val, img_thresh = cv2.threshold(img, 130, 255, cv2.THRESH_BINARY_INV)
val, temp_thresh = cv2.threshold(temp, 130, 255, cv2.THRESH_BINARY_INV)

_, img_contours, _ = cv2.findContours(img_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
_, temp_contours, _ = cv2.findContours(temp_thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

temp_contours.sort(key=lambda c:cv2.contourArea(c), reverse=True)
temp_contour = temp_contours[0]

best_contour = None
best_result = float('inf')

for ic in img_contours:
    d = cv2.matchShapes(ic, temp_contour, cv2.CONTOURS_MATCH_I1, 0)
    if d < best_result:
        best_result = d
        best_contour = ic

cv2.drawContours(img, best_contour, -1, (0, 255, 0), 3)
cv2.drawContours(temp, temp_contours[0], -1, (0, 255, 0), 3)


# print image
cv2.imshow('img', img)
cv2.imshow('temp', temp)

cv2.waitKey()



# Feature matching

MIN_MATCH_COUNT = 10

img1 = cv2.imread('box.png',0)          # queryImage
img2 = cv2.imread('box_in_scene.png',0) # trainImage


# Initiate SIFT detector
sift = cv2.ORB_create()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)


FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = 6, trees = 5)

search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1, des2, k=2)


# store all the good matches as per Lowe's ratio test.
good = []

for l in matches:
    if (len(l) < 2):
        continue
    m = l[0]
    n = l[1]

    if m.distance/n.distance < 0.7:
        good.append(m)


if len(good) > MIN_MATCH_COUNT:
    src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
    dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()

    h, w = img1.shape
    pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
    dst = cv2.perspectiveTransform(pts, M)

    img2 = cv2.polylines(img2, [np.int32(dst)], True, 0, 3, cv2.LINE_AA)


else:
    matchesMask = None
    print ('Not enough matches are found')


draw_params = dict(matchColor=(0, 255, 0),  # draw matches in green color
                   matchesMask=matchesMask,  # draw only inliers
                   flags=2)

img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)

plt.imshow(img3, 'gray'), plt.show()