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How to use match_keypoints method in Airtest

Best Python code snippet using Airtest

stitch.py

Source:stitch.py

...20import matplotlib.pyplot as plt 21import copy22import random as rand23from PIL import Image24def match_keypoints(kp1,kp2,des1,des2,src_img,des_img):25    26    """27    MATCH THE KEYPOINTS OF LEFT IMAGE AND RIGHT IMAGE28    INPUT: KEYPOINTS OF LEFT AND RIGHT IMAGE, DESCRIPTORS OF LEFT AND RIGHT IMAGE, MATCH KEYOINTS ON! 29    OUTPUT : SHOWS THE MATCH KEYPOINTS IN IMAGE 1 (LEFT ) AND IMAGE 2 (RIGHT). 30    """31    32# --------------------  SMALLEST DISTANCE MEASURED USING SSD -----------------------33    des_length1 = des1.shape[0]34    des_length2 = des2.shape[0]35    print("DES1",des_length1)36    print("DES2",des_length2)37    match_points = []38    for ix,iv in enumerate(des1):39        dist = np.linalg.norm(des1[ix] - des2[0])40        best_dist = dist41        sec_best = dist42        dist_best_idx = [ix,0]43        # print(dist_best_idx)44        for jx,jv in enumerate(des2):45            dist = np.linalg.norm(des1[ix]- des2[jx])46            if dist < best_dist:47                sec_best = best_dist48                best_dist = dist49                dist_best_idx = [ix, jx]50            elif dist < sec_best:51                sec_best = dist52        if (best_dist/sec_best) < 0.8:53            match_points.append(dist_best_idx)54    55    # print(match_points)56    match_points = np.array(match_points)57    match_points_img1 = match_points[:,0]58    match_points_img2 = match_points[:,1]59    # print(match_points_img1)60    # print(match_points_img2)61    keypoint_coord_img_1 = []62    keypoint_coord_img_2 = []63    64    for ii_idx,ii_val in enumerate(match_points_img1):65        keypoint_coord_img_1.append(kp1[ii_val].pt)66    for jj_idx,jj_val in enumerate(match_points_img2):67        keypoint_coord_img_2.append(kp2[jj_val].pt)68# ----------------------- MERGING ALL THE MATCHED KEYPOINTS -----------------------------------------69    keypoints_match = np.concatenate((np.array(keypoint_coord_img_1),np.array(keypoint_coord_img_2)),axis= 1 )70    # print(keypoints_match)71    keypoint_coord_img_1_x = []72    keypoint_coord_img_1_y = []73    74    keypoint_coord_img_2_x = []75    keypoint_coord_img_2_y = []76    77    for xx,yy in keypoint_coord_img_1:78        keypoint_coord_img_1_x.append(xx)79        keypoint_coord_img_1_y.append(yy)80    for xx1,yy1 in keypoint_coord_img_2:81        keypoint_coord_img_2_x.append(xx1)82        keypoint_coord_img_2_y.append(yy1)83    keypoint_coord_img_1_x  = np.asarray(keypoint_coord_img_1_x)84    keypoint_coord_img_1_y  = np.asarray(keypoint_coord_img_1_y)85    keypoint_coord_img_2_x  = np.asarray(keypoint_coord_img_2_x)86    keypoint_coord_img_2_y  = np.asarray(keypoint_coord_img_2_y)    87    88    # print(type(keypoint_coord_img_1_x))89    # plt.figure(figsize = (16,14))90    # plt.subplot(2,1,1)91    # plt.imshow(src_img)92    # plt.scatter(keypoint_coord_img_1_x,keypoint_coord_img_1_y)93    # plt.subplot(2,1,2)94    # plt.imshow(des_img)95    # plt.scatter(keypoint_coord_img_2_x,keypoint_coord_img_2_y)96    # plt.show()97    98    print("Press CTRL+W TO CLOSE THE IF THE IMAGE WINDOW SHOWING KEYPOINTS DISPLAY!! ")99    return keypoints_match100    101def RANSAC(match_keypoints,iterations):102    """103    GETS THE HOMOGRAPHY MATRIX IN ORDER TO REMOVE THE OUTLIERS! 104    INPUT: MATCHKEYPOINTS (N x 4) MATRIX, ITERATIONS105    OUTPUT: HOMOGRAPHY MATRIX (3X3)106    """107    no_of_keypoints = match_keypoints.shape[0]108    ones_ = np.ones((no_of_keypoints,1))109    a_ = match_keypoints110    best_H = []111    highest_inlier_count = 0112    corresponding_points = np.zeros((no_of_keypoints,2))113    114    115    point_1_ = np.append(match_keypoints[:,0:2],ones_,axis = 1)116    point_2_ = match_keypoints[:,2:4]117    for iteration in range(iterations):118        a = rand.choice(match_keypoints)119        b = rand.choice(match_keypoints)120        c = rand.choice(match_keypoints)121        d = rand.choice(match_keypoints)122        random_matchpairs = np.concatenate(([a],[b],[c],[d]),axis= 0)123        # print('HI',random_matchpairs)124        point_1 = np.float32(random_matchpairs[:,0:2])125        point_2 = np.float32(random_matchpairs[:,2:4])126        H = cv2.getPerspectiveTransform(point_1,point_2)127        current_inlier_count = 0128        # --- Solve for Ah = b ------- #129        xx_index = list()130        good_match = list()131        for i in range(no_of_keypoints):132            check_ = np.matmul(H,point_1_[i])133            corresponding_points[i] = (check_/check_[-1])[0:2]134            check = np.square(np.linalg.norm(corresponding_points[i] - point_2_[i]))135            if check < 0.6:136                current_inlier_count = current_inlier_count + 1137                xx_index.append(i)138                good_match.append([ a_[i][0], a_[i][1], a_[i][2], a_[i][3]])139        if current_inlier_count > highest_inlier_count:140            highest_inlier_count = current_inlier_count141            best_match = good_match142            print('Best matching.....',len(best_match))143            best_H = H144            145    146    print("Best matchin ..... DONE")147    print("RANSAC DONE!")148    return best_H149def main():150    all_images = []151    all_images.extend(glob.glob('sys.argv[1]' + '*.jpg'))152    all_images.sort()153    print(all_images)154    inp_img = []155    folder = sys.argv[1]156    for filename in os.listdir(folder):157        if filename == "panorama.jpg":158            print('Panorama Image already found, ignoring that image for stitching')159            continue160        img = cv2.imread(os.path.join(folder, filename))161        print(filename)162        dimension = (500,400)163        n = cv2.resize(img,dimension,interpolation=cv2.INTER_AREA)164        inp_img.append(n)165    # inp_img.sort()166    # print(inp_img)167    168    print(len(inp_img))169    sift = cv2.xfeatures2d.SIFT_create()170    # ---------------- DETECT AND DRAW THE KEYPOINTS USING SIFT --------------------171    img_nos = list()172    for i in range(len(inp_img)):173        img = kps, des = sift.detectAndCompute(inp_img[i],None)174        img_nos.append(img)175    176    img_match_coor = []177    print("IMAGE ORDERING......")178    #------------ CHECK FOR THE CORRESPONDING KEYPOINTS MATCH WITH RESPECT TO ONE IMAGE --------------------------179    for k in range(len(inp_img) - 1):180        for l in range(k+1, len(inp_img)):181            a01 = match_keypoints(img_nos[k][0],img_nos[l][0],img_nos[k][1],img_nos[l][1],inp_img[k],inp_img[l])182            if a01.shape == (1,4):183                print("N0 Points Match")184                continue185            print("KP1", len(img_nos[k][0]))186            print(a01.shape)187            print((k,l),a01.shape[0]/len(img_nos[k][0]))188            x = a01.shape[0]/len(img_nos[k][0])189            y = a01.shape[0]/len(img_nos[l][0])190            if (x > 0.04 and x < 0.9) and (y > 0.04 and y < 0.9):191                print("Selected")192                img_match_coor.append((k,l))193    print(img_match_coor)    194    print('Matching Image Pairs Achieved.')195    #------------------ ONCE WE GOT UNIQUE POINTS, CHECK FOR ENDPOINTS FROM THAT --------------------------------196    all_index = list()197    for a in range(len(img_match_coor)):198        for b in range(2):199            # print((a,b), "CHECK", img_match_coor[a][b])  #TO CHECK THE INDEX OF EACH ELEMENT OF THE TUPLE200            all_index.append(img_match_coor[a][b])201    # print(all_index) 202    all_index = set(all_index)203    print(all_index) #GIVES YOU UNIQUE ELEMENT OF THE TUPLE204    205    #------------------- CHECK FOR RESPECTIVE END POINTS POSSIBILITY--------------------------206    print("Checking for endpoints ......")207    end_points = list()208    for c in all_index:209        count = 0210        for x in range(len(img_match_coor)):211            if c in img_match_coor[x]:212                print('Current Point:',c)213                count = count + 1 214                print('Count',count)215                if count > 1:216                    print("More than required, remove: ", c)217                    end_points.remove(c)218                    continue219                else: 220                    print('check for end points.....',c)221                    end_points.append(c)222                223    print(end_points)224    print("Endpoints Acquired")225    final_order_list = []226    print("Arranging Image pairs in acatual(panoramic) order .................")227    for x in end_points:228        final_order_list.append(x)229        # if len(img_match_coor) == 1:230        #     print("NIKAL LAVDE")231        #     break232        for y in range(len(img_match_coor)):233            if len(img_match_coor) == 1:234                break235            for z in range(len(img_match_coor[y])):236                # print((y,z))237                # if len(img_match_coor) == 1:238                #     print("CONTROLL")239                #     break240                if x in img_match_coor[y]:241                    # print("CHECK", img_match_coor[y])242                    if x != img_match_coor[y][z]:243                        x_cores = img_match_coor[y][z]244                        final_order_list.append(x_cores)245                        print(x,"CHRCK",img_match_coor[y],"YEHS HAI X CORES", x_cores)246                        img_match_coor.remove(img_match_coor[y])247                        print(img_match_coor)248                        for io_ in range(len(img_match_coor)):249                            if len(img_match_coor) == 1:250                                break251                            for iz in range(len(img_match_coor[io_])):252                                if x_cores in img_match_coor[io_]:253                                    if x_cores != img_match_coor[io_][iz]:254                                        x_cores_up = img_match_coor[io_][iz]255                                        final_order_list.append(x_cores_up)256                                        print(x_cores,"CHECK NEW",img_match_coor[io_],"YEH HAI UPDATED",x_cores_up)257                                        img_match_coor.remove(img_match_coor[io_])258                                        print(img_match_coor)259                                        for io1_ in range(len(img_match_coor)):260                                            if len(img_match_coor) == 1:261                                                break262                                            for iz1 in range(len(img_match_coor[io1_])):263                                                if x_cores_up in img_match_coor[io1_]:264                                                    if x_cores_up != img_match_coor[io1_][iz1]:265                                                        x_cores_up_up = img_match_coor[io1_][iz1]266                                                        final_order_list.append(x_cores_up_up)267                                                        print(x_cores_up,"CHECK EKDUM NEW",img_match_coor[io1_],"YEH HAI UDDDD",x_cores_up_up)268                                                        img_match_coor.remove(img_match_coor[io1_])269                                                        print(img_match_coor)270                                        if len(img_match_coor) == 1:271                                            break272                        print("LEN", len(img_match_coor))273                        if len(img_match_coor) == 1:274                            break275                        276    277    print(final_order_list)278    homography_list = list()279    print("Arranging Image pairs in acatual(panoramic) order ................. In process")280    281    len_ord_fin = len(final_order_list)282    tkp1 = img_nos[final_order_list[0]][0]283    tdes1 = img_nos[final_order_list[0]][1]284    tkp2 = img_nos[final_order_list[1]][0]285    tdes2 = img_nos[final_order_list[1]][1]286    x1 = match_keypoints(tkp1,tkp2,tdes1,tdes2,inp_img[final_order_list[0]],inp_img[final_order_list[1]])287    y1 = (RANSAC(x1,400))288    check1_  = np.matmul(y1, np.array([0, 0, 1]))289    if check1_[0]/check1_[2] < 0:290        print(check1_[0]/check1_[2])291    else:292        final_order_list.reverse()293        print(check1_[0]/check1_[2])294    # print(f'This is final list now: {final_order_list}')295    print("This is final list now:", final_order_list)296    # t_match = match_keypoints()297    print("Image ORDERING ....... COMPLETED")298    ########################------------------------- MAIN STICHING PROGRAM STARTS HERE ---------------------------------------------------##########################299    print("Image Stitching ..... Started")300    def panorama_(i1, i2):301        """302        THIS FUNCTION DOES THE STITCHING OF TWO IMAGES303        INPUT: TWO IMAGE304        OUTPUT: RESULTED IMAGE305        """306        kp1, des1 = sift.detectAndCompute(i1,None)307        kp2, des2 = sift.detectAndCompute(i2,None)308        correspondence_ = match_keypoints(kp1,kp2,des1,des2,i1,i2)309        H  = RANSAC(correspondence_,400)310        H = np.linalg.inv(H)311        stitched_ = cv2.warpPerspective(i2,H,(i1.shape[1]+ i2.shape[1],i1.shape[0]))312        stitched_[0:i1.shape[0],0:i1.shape[1]] = i1313        # plt.figure(figsize=(16,14)) 314        # plt.imshow(stitched_)315        # plt.show()        316        return stitched_317    final_order_list.reverse()318    panorama = inp_img[final_order_list[0]]319    320    img_width = 0321    img_height = 0322    for index_,image_ in enumerate(final_order_list[:-1]):323        kp_last_image,des_last_image = sift.detectAndCompute(panorama,None)324        previous_image = inp_img[final_order_list[index_+1]]325        kp_previous_image, descriptor_previous_image  = sift.detectAndCompute(previous_image,None)326        xx = match_keypoints(kp_last_image,kp_previous_image,des_last_image,descriptor_previous_image,panorama,previous_image)327        H = RANSAC(xx,500)328        # print(h)329        img_width += panorama.shape[1] + previous_image.shape[1]330        img_height = previous_image.shape[0]331        panorama = cv2.warpPerspective(panorama,(H),(img_width,img_height))332        panorama[0:previous_image.shape[0], 0:previous_image.shape[1]] = previous_image 333        # plt.figure()334        # plt.title("JOILE")335        # plt.imshow(panorama)336        # print("LE MARO")337    plt.figure()338    plt.title("Magnifincento!")339    plt.imshow(panorama)340    plt.show()...

4-draw-matches.py

Source:4-draw-matches.py

1#!/usr/bin/env python32import argparse3import os4import tempfile5import cv26import utils7if __name__ == '__main__':8    parser = argparse.ArgumentParser()9    parser.add_argument('gw_20p_wannot_dirpath')10    parser.add_argument('matches_filepath')11    args = parser.parse_args()12    matches = utils.load_matches(args.matches_filepath)13    output_dirpath = tempfile.mkdtemp()14    # extract top-k matches15    k = 2016    print(f'Extracting top-{k} matches in each page...')17    for page_filename, page_matches in matches.items():18        top_k_page_matches = sorted(page_matches, key=lambda x: x[1])[:k]19        assert len(top_k_page_matches) <= k20        page_image = cv2.imread(f'{args.gw_20p_wannot_dirpath}/{page_filename}')21        for i, match_scored in enumerate(top_k_page_matches):22            match = match_scored[0]23            # keep only non-empty keypoints24            match_keypoints = [keypoint for keypoint in match if keypoint]25            match_keypoints = utils.namedtuple_keypoints_to_cv2(match_keypoints)26            # draw keypoints on the original page27            page_image = cv2.drawKeypoints(28                page_image, match_keypoints, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)29            # draw a box around the match30            offset_pixel = 2531            x_min = float('inf')32            y_min = float('inf')33            x_max = 034            y_max = 035            for keypoint in match_keypoints:36                x = int(keypoint.pt[0])37                y = int(keypoint.pt[1])38                if x < x_min:39                    x_min = x40                if x > x_max:41                    x_max = x42                if y < y_min:43                    y_min = y44                if y > y_max:45                    y_max = y46            x_min += -offset_pixel47            y_min += -offset_pixel48            x_max += +offset_pixel49            y_max += +offset_pixel50            cv2.rectangle(page_image, (x_min, y_min), (x_max, y_max), (0, 0, 255), 2)51        page_image_filepath = f'{output_dirpath}/{page_filename}.png'52        print(f'Saving: {page_image_filepath}')...

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