import sys

import cv2

import numpy as np

#from google.colab.patches import cv2_imshow

def draw_matches(img1, keypoints1, img2, keypoints2, matches):

r, c = img1.shape[:2]

r1, c1 = img2.shape[:2]

# Create a blank image with the size of the first image + second image

output_img = np.zeros((max([r, r1]), c+c1, 3), dtype='uint8')

output_img[:r, :c, :] = np.dstack([img1, img1, img1])

output_img[:r1, c:c+c1, :] = np.dstack([img2, img2, img2])

# Go over all of the matching points and extract them

for match in matches:

img1_idx = match.queryIdx

img2_idx = match.trainIdx

(x1, y1) = keypoints1[img1_idx].pt

(x2, y2) = keypoints2[img2_idx].pt

# Draw circles on the keypoints

cv2.circle(output_img, (int(x1),int(y1)), 4, (0, 255, 255), 1)

cv2.circle(output_img, (int(x2)+c,int(y2)), 4, (0, 255, 255), 1)

# Connect the same keypoints

cv2.line(output_img, (int(x1),int(y1)), (int(x2)+c,int(y2)), (0, 255, 255), 1)

return output_img

def warpImages(img1, img2, H):

rows1, cols1 = img1.shape[:2]

rows2, cols2 = img2.shape[:2]

list_of_points_1 = np.float32([[0,0], [0, rows1],[cols1, rows1], [cols1, 0]]).reshape(-1, 1, 2)

temp_points = np.float32([[0,0], [0,rows2], [cols2,rows2], [cols2,0]]).reshape(-1,1,2)

# When we have established a homography we need to warp perspective

# Change field of view

list_of_points_2 = cv2.perspectiveTransform(temp_points, H)

list_of_points = np.concatenate((list_of_points_1,list_of_points_2), axis=0)

[x_min, y_min] = np.int32(list_of_points.min(axis=0).ravel() - 0.5)

[x_max, y_max] = np.int32(list_of_points.max(axis=0).ravel() + 0.5)

translation_dist = [-x_min,-y_min]

H_translation = np.array([[1, 0, translation_dist[0]], [0, 1, translation_dist[1]], [0, 0, 1]])

output_img = cv2.warpPerspective(img2, H_translation.dot(H), (x_max-x_min, y_max-y_min))

output_img[translation_dist[1]:rows1+translation_dist[1], translation_dist[0]:cols1+translation_dist[0]] = img1

return output_img

img1 = cv2.imread("tmp/img4.jpeg")

img2 = cv2.imread("tmp/img5.jpeg")

img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)

img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

orb = cv2.ORB_create(nfeatures=2000)

# Find the key points and descriptors with ORB

keypoints1, descriptors1 = orb.detectAndCompute(img1, None)

keypoints2, descriptors2 = orb.detectAndCompute(img2, None)

# Create a BFMatcher object.

# It will find all of the matching keypoints on two images

bf = cv2.BFMatcher_create(cv2.NORM_HAMMING)

# Find matching points

matches = bf.knnMatch(descriptors1, descriptors2,k=2)

print(keypoints1[0].pt)

print(keypoints1[0].size)

print("Descriptor of the first keypoint: ")

print(descriptors1[0])

all_matches = []

for m, n in matches:

all_matches.append(m)

img3 = draw_matches(img1_gray, keypoints1, img2_gray, keypoints2, all_matches[:30])

good = []

for m, n in matches:

if m.distance < 0.6 * n.distance:

good.append(m)

cv2.drawKeypoints(img1, [keypoints1[m.queryIdx] for m in good], None, (255, 0, 255))

MIN_MATCH_COUNT = 5

if len(good) > MIN_MATCH_COUNT:

# Convert keypoints to an argument for findHomography

src_pts = np.float32([ keypoints1[m.queryIdx].pt for m in good]).reshape(-1,1,2)

dst_pts = np.float32([ keypoints2[m.trainIdx].pt for m in good]).reshape(-1,1,2)

# Establish a homography

M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)

result = warpImages(img2, img1, M)

cv2.imwrite("resultado00.jpeg", result)