From 25e365446b78699fa0c253e7de5204df9aff7f48 Mon Sep 17 00:00:00 2001
From: Simon Mayer <simon.mayer@onb.ac.at>
Date: Thu, 15 Apr 2021 14:36:52 +0200
Subject: [PATCH] added code for extracting registration data with SIFT feature
 detection

---
 stitching/download_imgs.py     |  18 +++
 stitching/expected_matches.txt |  37 ++++++
 stitching/plots/avg_conf.png   | Bin 0 -> 44233 bytes
 stitching/scan.py              | 223 ++++++++++++++++++++++++++++++---
 stitching/stitch_data.json     |   1 +
 5 files changed, 259 insertions(+), 20 deletions(-)
 create mode 100644 stitching/download_imgs.py
 create mode 100644 stitching/expected_matches.txt
 create mode 100644 stitching/plots/avg_conf.png
 create mode 100644 stitching/stitch_data.json

diff --git a/stitching/download_imgs.py b/stitching/download_imgs.py
new file mode 100644
index 0000000..b4aea9b
--- /dev/null
+++ b/stitching/download_imgs.py
@@ -0,0 +1,18 @@
+import requests
+import pathlib
+
+
+def dl_at_qual(pct):
+    for num in range(9, 51):
+        path = pathlib.Path(f'imgs/danube_{pct}_pct/{num}.jpg')
+        if path.exists():
+            return
+        else:
+            if num == 9:
+                num = '09'
+            r = requests.get(f'https://iiif.onb.ac.at/images/REPO/7502199/000000{num}.jpg/full/pct:{pct}/0/native.jpg')
+            open(path, 'wb').write(r.content)
+
+
+if __name__ == '__main__':
+    dl_at_qual(10)
diff --git a/stitching/expected_matches.txt b/stitching/expected_matches.txt
new file mode 100644
index 0000000..150bc35
--- /dev/null
+++ b/stitching/expected_matches.txt
@@ -0,0 +1,37 @@
+[['9.jpg', '10.jpg'],
+['10.jpg', '11.jpg'],
+['11.jpg', '12.jpg'],
+['12.jpg', '13.jpg'],
+['13.jpg', '14.jpg'],
+['14.jpg', '15.jpg'],
+['15.jpg', '16.jpg'],
+['16.jpg', '17.jpg'],
+['17.jpg', '18.jpg'],
+['18.jpg', '19.jpg'],
+['19.jpg', '20.jpg'],
+['20.jpg', '21.jpg'],
+['21.jpg', '22.jpg'],
+['22.jpg', '23.jpg'],
+['23.jpg', '24.jpg'],
+['24.jpg', '25.jpg'],
+['25.jpg', '26.jpg'],
+['26.jpg', '27.jpg'],
+['27.jpg', '28.jpg'],
+['28.jpg', '29.jpg'],
+['29.jpg', '30.jpg'],
+['30.jpg', '31.jpg'],
+['31.jpg', '32.jpg'],
+['32.jpg', '33.jpg'],
+['33.jpg', '34.jpg'],
+['34.jpg', '35.jpg'],
+['42.jpg', '43.jpg'],
+['43.jpg', '44.jpg'],
+['44.jpg', '45.jpg'],
+['45.jpg', '46.jpg'],
+['46.jpg', '47.jpg'],
+['47.jpg', '48.jpg'],
+['48.jpg', '49.jpg'],
+['49.jpg', '50.jpg'],
+['36.jpg', '37.jpg'],
+['39.jpg', '40.jpg'],
+['40.jpg', '41.jpg']]
\ No newline at end of file
diff --git a/stitching/plots/avg_conf.png b/stitching/plots/avg_conf.png
new file mode 100644
index 0000000000000000000000000000000000000000..17c49e4c31776ce1f7b6b360e2824ae394f6cf82
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HcmV?d00001

diff --git a/stitching/scan.py b/stitching/scan.py
index 904e7ef..726a5ea 100644
--- a/stitching/scan.py
+++ b/stitching/scan.py
@@ -7,6 +7,7 @@ Adapted from https://raw.githubusercontent.com/opencv/opencv/master/samples/pyth
 import cv2 as cv
 import numpy as np
 import matplotlib.pyplot as plt
+import json
 
 
 def resize_image(img, scale):
@@ -130,6 +131,7 @@ def get_warped_masks_and_images(imgs, cameras, warp_scale, seam_aspect):
         K[1, 1] *= swa
         K[1, 2] *= swa
         corner, image_wp = warper.warp(imgs[idx], K, cameras[idx].R, cv.INTER_LINEAR, cv.BORDER_REFLECT)
+        # corner, image_wp = warper.warp(imgs[idx], K, cameras[idx].R, cv.INTER_NEAREST, cv.BORDER_CONSTANT)
         corners.append(corner)
         images_warped.append(image_wp)
         p, mask_wp = warper.warp(masks[idx], K, cameras[idx].R, cv.INTER_NEAREST, cv.BORDER_CONSTANT)
@@ -186,16 +188,17 @@ def blend_images(corners, imgs, seam_msks, msks_w, comp):
 
 
 def get_reg_data(imgs, img_names, reg_megapix=0.6):
-    reg_scale = get_scale(imgs[0], reg_megapix)
-    imgs_med_res = [resize_image(img, reg_scale) for img in imgs]
+    if reg_megapix == -1:
+        imgs_med_res = imgs
+    else:
+        reg_scale = get_scale(imgs[0], reg_megapix)
+        imgs_med_res = [resize_image(img, reg_scale) for img in imgs]
     features = find_features(imgs_med_res)
-    matches = match_features(features)
-    # for match in matches:
-    #     print(match.num_inliers, match.confidence, match.src_img_idx, match.dst_img_idx)
+    matches = match_features(features, match_conf=0.35)
     indices, img_names_subset, img_subset, features_subset, matches_subset = get_biggest_subset(
-        img_names, imgs_med_res, features, matches, conf_thresh=0.3, match_conf=0.3)
+        img_names, imgs_med_res, features, matches, conf_thresh=0.6, match_conf=0.35)
 
-    graph = get_matches_graph(img_names_subset, matches_subset, conf_thresh=0.3)
+    graph = get_matches_graph(img_names_subset, matches_subset, conf_thresh=0.6)
     cameras = estimate_cameras(features_subset, matches_subset)
     cameras = refine_cameras(features_subset, matches_subset, cameras, 'xxxxx')
     return indices, graph, cameras
@@ -203,8 +206,14 @@ def get_reg_data(imgs, img_names, reg_megapix=0.6):
 
 def prepare_reg_data(imgs, indices, reg_megapix=0.6, seam_megapix=0.1):
     imgs_subset = [imgs[i[0]] for i in indices]
-    reg_scale = get_scale(imgs[0], megapix=reg_megapix)
-    seam_scale = get_scale(imgs[0], megapix=seam_megapix)
+    if reg_megapix == -1:
+        reg_scale = 1
+    else:
+        reg_scale = get_scale(imgs[0], megapix=reg_megapix)
+    if seam_megapix == -1:
+        seam_scale = 1
+    else:
+        seam_scale = get_scale(imgs[0], megapix=seam_megapix)
     return imgs_subset, reg_scale, seam_scale
 
 
@@ -225,21 +234,195 @@ def full_stitching_pipeline(imgs, img_names):
     indices, graph, cameras = get_reg_data(imgs, img_names, reg_megapix=0.6)
     imgs_subset, reg_scale, seam_scale = prepare_reg_data(imgs, indices, reg_megapix=0.6, seam_megapix=0.1)
     result, result_mask = compose_from_reg_data(imgs_subset, cameras, reg_scale, seam_scale)
-    # print(graph)
     return result
 
 
+def debug_stitching_pipeline(imgs, img_names):
+    features = find_features(imgs)
+    matches = match_features(features, match_conf=0.3)
+    # for match in matches:
+    #     print(match.getInliers())
+    #     print(match.getMatches())
+    #     print(match.confidence)
+    #     print(match.src_img_idx, match.dst_img_idx)
+    #     print(match.num_inliers)
+    #     print(match.H)
+    graph = get_matches_graph(img_names, matches, conf_thresh=0.3)
+    print(graph)
+    cameras = estimate_cameras(features, matches)
+    # cameras = refine_cameras(features, matches, cameras, 'xxxxx')
+    for cam in cameras:
+        print(cam.R)
+    corners, images_w, masks_w = get_warped_masks_and_images(imgs, cameras, 1, 1)
+    print(corners)
+    corners = [(-3788, -362), (-1872, -178), (0, 0), (1889, -1135), (3450, -2492)]
+    # corners = [(0, 0), (1947, -556)] #- (2914, 2736) + (2879, 2733) = (-35, -3), (2305, 823) - (2335, 826) = (-30, -3)
+    # corners = [(0, 0), (1912, -559)]
+    # corners = [(0, 0), (1583, -88)]
+    # cv.imwrite('32_w.jpg', images_w[0])
+    # cv.imwrite('32_m_w.jpg', masks_w[0])
+    # cv.imwrite('33_w.jpg', images_w[1])
+    # cv.imwrite('33_m_w.jpg', masks_w[1])
+    seam_masks = find_seams(corners, images_w, masks_w)
+    cv.imwrite('24_sm.jpg', seam_masks[0])
+    cv.imwrite('25_sm.jpg', seam_masks[1])
+    sizes = get_sizes(images_w)
+    blender = cv.detail_MultiBandBlender()
+    dst_sz = cv.detail.resultRoi(corners=corners, sizes=sizes)
+    blend_strength = 5
+    blend_width = np.sqrt(dst_sz[2] * dst_sz[3]) * blend_strength / 100
+    blender.setNumBands((np.log(blend_width) / np.log(2.) - 1.).astype(np.int32))
+    blender.prepare(dst_sz)
+    # comp = estimate_exposure_compensation(corners, images_w, masks_w)
+    for idx in range(len(images_w)):
+        # comp.apply(idx, corners[idx], images_w[idx], masks_w[idx])
+        img_s = images_w[idx].astype(np.int16)
+        blender.feed(cv.UMat(img_s), seam_masks[idx], corners[idx])
+    result, result_mask = blender.blend(None, None)
+    cv.imwrite('test.jpg', result)
+    cv.imwrite('test_m.jpg', result_mask)
+
+
+def feature_testing(img_names, comp, match_conf=0.3):
+    features = []
+    feat_finders = [cv.AKAZE_create(), cv.BRISK_create(), cv.KAZE_create(), cv.ORB_create(), cv.SIFT_create()]
+    for name in img_names:
+        img = cv.imread(name)
+        feat_lis = []
+        for finder in feat_finders:
+            feat = cv.detail.computeImageFeatures2(finder, img)
+            feat_lis.append(feat)
+        features.append(feat_lis)
+    features_transposed = list(map(list, zip(*features)))
+    matches_lis = [match_features(feat, match_conf=match_conf) for feat in features_transposed]
+    H_lis = []
+    for m in matches_lis:
+        c = []
+        for info in m:
+            if info.src_img_idx + 1 == info.dst_img_idx:
+                c.append(info)
+        H_lis.append(c)
+    # H_transposed = list(map(list, zip(*H_lis)))
+    # plt.plot(list(range(len(img_names) - 1)), confidences_transposed)
+    # plt.title(f'Confidences of feature detection algorithms at match_conf={match_conf}')
+    # plt.legend(['AKAZE', 'BRISK', 'KAZE', 'ORB', 'SIFT'])
+    # plt.savefig(f'comp_{comp}_match_conf_{match_conf}.png')
+    # plt.clf()
+    return H_lis
+
+
+def match_sift_features(img_names, match_conf=0.4):
+    features = []
+    feat_finder = cv.SIFT_create()
+    for name in img_names:
+        img = cv.imread(name)
+        feat = cv.detail.computeImageFeatures2(feat_finder, img)
+        features.append(feat)
+    matches = match_features(features, match_conf=match_conf)
+    overlap_matches = []
+    for match in matches:
+        if match.src_img_idx + 1 == match.dst_img_idx:
+            overlap_matches.append(match)
+    return overlap_matches
+
+
+def extract_from_R_mat(R_mat):
+    scale = np.sqrt(R_mat[0, 0] ** 2 + R_mat[0, 1] ** 2)
+    angle = np.arcsin(R_mat[1, 0] / scale) * 180 / np.pi
+    t_x = R_mat[0, 2]
+    t_y = R_mat[1, 2]
+    return angle, scale, t_x, t_y
+
+
+def truncate_path(s):
+    return s.replace('imgs/danube_10_pct/', '')
+
+
+def get_match_details(img_names, matches_lis):
+    details_lis = []
+    for match in matches_lis:
+        details = {}
+        angle, scale, x, y = extract_from_R_mat(match.H)
+        src_idx = match.src_img_idx
+        dst_idx = match.dst_img_idx
+        details['src_name'] = truncate_path(img_names[src_idx])
+        details['src_shape'] = get_img_shape(img_names[src_idx])
+        details['dst_name'] = truncate_path(img_names[dst_idx])
+        details['dst_shape'] = get_img_shape(img_names[dst_idx])
+        details['angle'] = angle
+        details['scale'] = scale
+        details['x'] = x
+        details['y'] = y
+        details_lis.append(details)
+    return details_lis
+
+
+def get_img_shape(img_name):
+    img = cv.imread(img_name)
+    return img.shape[1], img.shape[0]
+
+
 if __name__ == '__main__':
-    # img_names = [f'pano_{i}.jpg' for i in range(1, 4)]
-    # img_names = ['left.png', 'right.png']
-    # img_names = ['left2.png', 'right2.png']
-    img_names = ['wien_low1.jpg', 'prague2.jpg']
-    # img_names = ['prague1.jpg', 'prague2.jpg']
-    imgs = [cv.imread(f'imgs/{name}') for name in img_names]
+    path = 'imgs/danube_10_pct'
+    comp_1 = [f'{path}/{i}.jpg' for i in range(9, 36)]
+    comp_2 = [f'{path}/{i}.jpg' for i in range(42, 51)]
+    comp_3 = [f'{path}/39.jpg', f'{path}/40.jpg', f'{path}/41.jpg']
+    comp_4 = [f'{path}/36.jpg', f'{path}/37.jpg']
+    comp_5 = [f'{path}/38.jpg']
+    img_names = [f'imgs/danube_10_pct/{i}.jpg' for i in range(9, 51)]
+    # imgs = [cv.imread(f'imgs/{name}') for name in img_names]
+    debug_mode = True
     try:
-        result = full_stitching_pipeline(imgs, img_names)
-        out_name = 'imgs_out/wien_test.jpg'
-        cv.imwrite(out_name, result)
-        print('Stitched image saved as ' + out_name)
+        if debug_mode:
+            # test_scale = ['imgs/danube_10_pct/13.jpg', 'imgs/danube_10_pct/14_scaled_down.jpg']
+            all_matches = [match_sift_features(eval(f'comp_{i}'), match_conf=0.4) for i in range(1, 5)]
+            match_details = [get_match_details(eval(f'comp_{i+1}'), all_matches[i]) for i in range(0, 4)]
+            components = [{'component': i+1, 'data': match_details[i]} for i in range(0, 4)]
+            for comp in components:
+                print(comp)
+            with open('stitch_data.json', 'w') as fp:
+                json.dump(components, fp)
+            # with open('stitch_data.json', 'r') as fp:
+            #     components = json.load(fp)
+            # H_2 = feature_testing(comp_2, 2, match_conf=0.4)
+            # H_3 = feature_testing(comp_3, 3, match_conf=0.4)
+            # H_4 = feature_testing(comp_4, 4, match_conf=0.4)
+            # conf_1 = feature_testing(comp_1, 1, match_conf=0.8)
+            # conf_2 = feature_testing(comp_2, 2, match_conf=0.8)
+            # conf_3 = feature_testing(comp_3, 3, match_conf=0.8)
+            # conf_4 = feature_testing(comp_4, 4, match_conf=0.8)
+            # with open('confs_match_conf_0.8.csv', 'w') as file:
+            #     json.dump([conf_1, conf_2, conf_3, conf_4], file)
+            # confs = []
+            # for i in range(2, 9):
+            #     with open(f'confs_match_conf_0.{i}.csv', 'r') as file:
+            #         lis = json.load(file)
+            #     flat_lis = [item for sublist in lis for item in sublist]
+            #     avg_conf = np.mean(flat_lis, axis=0)
+            #     confs.append(avg_conf)
+            # plt.plot(np.arange(0.2, 0.8, 0.1), confs)
+            # plt.legend(['AKAZE', 'BRISK', 'KAZE', 'ORB', 'SIFT'])
+            # plt.title('Average confidence for different match_conf')
+            # plt.savefig('avg_conf.png')
+            # orb = cv.ORB.create()
+            # crop1 = imgs[0][0:2765, 1920:3246]
+            # crop2 = imgs[1][0:3089, 0:1170]
+            # imgs = [crop1, crop2]
+            # debug_stitching_pipeline(imgs, img_names)
+            # kp1, des1 = orb.detectAndCompute(imgs[0], None)
+            # kp2, des2 = orb.detectAndCompute(imgs[1], None)
+            # feat1 = cv.detail.computeImageFeatures2(orb, imgs[0])
+            # feat2 = cv.detail.computeImageFeatures2(orb, imgs[1])
+            # afm = cv.detail_AffineBestOf2NearestMatcher.create()
+            # matches_info = afm.apply(feat1, feat2)
+            # matches = matches_info.getMatches()
+            # matches = sorted(matches, key=lambda x: x.distance)
+            # img = cv.drawMatches(imgs[0], kp1, imgs[1], kp2, matches[:20], None)
+            # cv.imwrite('imgs_out/matches3.jpg', img)
+        else:
+            imgs = [cv.imread(name) for name in img_names]
+            result = full_stitching_pipeline(imgs, img_names)
+            res_name = 'area.jpg'
+            cv.imwrite(res_name, result)
     except SystemExit:
         print('program execution stopped!')
diff --git a/stitching/stitch_data.json b/stitching/stitch_data.json
new file mode 100644
index 0000000..9761973
--- /dev/null
+++ b/stitching/stitch_data.json
@@ -0,0 +1 @@
+[{"component": 1, "data": [{"src_name": "9.jpg", "src_shape": [845, 627], "dst_name": "10.jpg", "dst_shape": [916, 630], "angle": -0.08323251825809101, "scale": 1.0012847517044934, "x": -725.8677796027105, "y": 1.327231526027312}, {"src_name": "10.jpg", "src_shape": [916, 630], "dst_name": "11.jpg", "dst_shape": [908, 758], "angle": -0.3874085856970733, "scale": 0.9993879049233608, "x": -727.5777707043392, "y": 4.190702764230509}, {"src_name": "11.jpg", "src_shape": [908, 758], "dst_name": "12.jpg", "dst_shape": [884, 756], "angle": 0.07714987376645693, "scale": 1.0001919853845924, "x": -752.6976600173008, "y": -2.722047131190443}, {"src_name": "12.jpg", "src_shape": [884, 756], "dst_name": "13.jpg", "dst_shape": [924, 703], "angle": -0.04060580582383663, "scale": 1.0004665056559117, "x": -770.8630238226102, "y": 71.61996380936549}, {"src_name": "13.jpg", "src_shape": [924, 703], "dst_name": "14.jpg", "dst_shape": [1179, 824], "angle": 19.03415666322512, "scale": 0.9966014463696012, 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