train_find_image.py

"""
Finds a specific image within another image.
Author: Landon Wiedenman
"""

import sys
sys.path.append('/usr/local/Cellar/opencv/3.3.1_1/lib/python3.6/site-packages')
import cv2
import numpy as np
from matplotlib import pyplot as plt


def find_image(image_find_path, image_source_path):
    """ Finds image_find_path within image_source_path """

    MIN_MATCH_COUNT = 10

    img1 = cv2.imread(image_source_path, 0)          # queryImage
    img2 = cv2.imread(image_find_path, 0)    # trainImage

    # Initiate SIFT detector
    sift = cv2.xfeatures2d.SIFT_create()

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

    FLANN_INDEX_KDTREE = 0
    index_params = dict(algorithm=FLANN_INDEX_KDTREE, 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 m, n in matches:
        if m.distance < 0.7*n.distance:
            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, 255, 3, cv2.LINE_AA)

    else:
        print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT))
        matchesMask = None

    draw_params = dict(matchColor=(0, 255, 0),  # draw matches in green color
                       singlePointColor=None,
                       matchesMask=matchesMask,  # draw only inliers
                       flags=2)

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

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