import argparse import gradio as gr from hloc import extract_features from extra_utils.utils import ( matcher_zoo, device, match_dense, match_features, get_model, get_feature_model, display_matches, ) def run_matching( match_threshold, extract_max_keypoints, keypoint_threshold, key, image0, image1 ): # image0 and image1 is RGB mode if image0 is None or image1 is None: raise gr.Error("Error: No images found! Please upload two images.") model = matcher_zoo[key] match_conf = model["config"] # update match config match_conf["model"]["match_threshold"] = match_threshold match_conf["model"]["max_keypoints"] = extract_max_keypoints matcher = get_model(match_conf) if model["dense"]: pred = match_dense.match_images( matcher, image0, image1, match_conf["preprocessing"], device=device ) del matcher extract_conf = None else: extract_conf = model["config_feature"] # update extract config extract_conf["model"]["max_keypoints"] = extract_max_keypoints extract_conf["model"]["keypoint_threshold"] = keypoint_threshold extractor = get_feature_model(extract_conf) pred0 = extract_features.extract( extractor, image0, extract_conf["preprocessing"] ) pred1 = extract_features.extract( extractor, image1, extract_conf["preprocessing"] ) pred = match_features.match_images(matcher, pred0, pred1) del extractor fig, num_inliers = display_matches(pred) del pred return ( fig, {"matches number": num_inliers}, {"match_conf": match_conf, "extractor_conf": extract_conf}, ) def ui_change_imagebox(choice): return {"value": None, "source": choice, "__type__": "update"} def ui_reset_state( match_threshold, extract_max_keypoints, keypoint_threshold, key, image0, image1 ): match_threshold = 0.2 extract_max_keypoints = 1000 keypoint_threshold = 0.015 key = list(matcher_zoo.keys())[0] image0 = None image1 = None return ( match_threshold, extract_max_keypoints, keypoint_threshold, key, image0, image1, {"value": None, "source": "upload", "__type__": "update"}, {"value": None, "source": "upload", "__type__": "update"}, "upload", None, {}, {}, ) def run(config): with gr.Blocks(css="footer {visibility: hidden}") as app: gr.Markdown( """

Image Matching WebUI

""" ) with gr.Row(equal_height=False): with gr.Column(): with gr.Row(): matcher_list = gr.Dropdown( choices=list(matcher_zoo.keys()), value="disk+lightglue", label="Matching Model", interactive=True, ) match_image_src = gr.Radio( ["upload", "webcam", "canvas"], label="Image Source", value="upload", ) with gr.Row(): match_setting_threshold = gr.Slider( minimum=0.0, maximum=1, step=0.001, label="Match threshold", value=0.1, ) match_setting_max_features = gr.Slider( minimum=10, maximum=10000, step=10, label="Max number of features", value=1000, ) # TODO: add line settings with gr.Row(): detect_keypoints_threshold = gr.Slider( minimum=0, maximum=1, step=0.001, label="Keypoint threshold", value=0.015, ) detect_line_threshold = gr.Slider( minimum=0.1, maximum=1, step=0.01, label="Line threshold", value=0.2, ) # matcher_lists = gr.Radio( # ["NN-mutual", "Dual-Softmax"], # label="Matcher mode", # value="NN-mutual", # ) with gr.Row(): input_image0 = gr.Image( label="Image 0", type="numpy", interactive=True, image_mode="RGB", ) input_image1 = gr.Image( label="Image 1", type="numpy", interactive=True, image_mode="RGB", ) with gr.Row(): button_reset = gr.Button(label="Reset", value="Reset") button_run = gr.Button( label="Run Match", value="Run Match", variant="primary" ) with gr.Accordion("Open for More!", open=False): gr.Markdown( f"""

Supported Algorithms

{", ".join(matcher_zoo.keys())} """ ) # collect inputs inputs = [ match_setting_threshold, match_setting_max_features, detect_keypoints_threshold, matcher_list, input_image0, input_image1, ] # Add some examples with gr.Row(): examples = [ [ 0.1, 2000, 0.015, "disk+lightglue", "datasets/sacre_coeur/mapping/71295362_4051449754.jpg", "datasets/sacre_coeur/mapping/93341989_396310999.jpg", ], [ 0.1, 2000, 0.015, "loftr", "datasets/sacre_coeur/mapping/03903474_1471484089.jpg", "datasets/sacre_coeur/mapping/02928139_3448003521.jpg", ], [ 0.1, 2000, 0.015, "disk", "datasets/sacre_coeur/mapping/10265353_3838484249.jpg", "datasets/sacre_coeur/mapping/51091044_3486849416.jpg", ], [ 0.1, 2000, 0.015, "topicfm", "datasets/sacre_coeur/mapping/44120379_8371960244.jpg", "datasets/sacre_coeur/mapping/93341989_396310999.jpg", ], [ 0.1, 2000, 0.015, "superpoint+superglue", "datasets/sacre_coeur/mapping/17295357_9106075285.jpg", "datasets/sacre_coeur/mapping/44120379_8371960244.jpg", ], ] # Example inputs gr.Examples( examples=examples, inputs=inputs, outputs=[], fn=run_matching, cache_examples=True, label="Examples (click one of the images below to Run Match)", ) with gr.Column(): output_mkpts = gr.Image(label="Keypoints Matching", type="numpy") matches_result_info = gr.JSON(label="Matches Statistics") matcher_info = gr.JSON(label="Match info") # callbacks match_image_src.change( fn=ui_change_imagebox, inputs=match_image_src, outputs=input_image0 ) match_image_src.change( fn=ui_change_imagebox, inputs=match_image_src, outputs=input_image1 ) # collect outputs outputs = [ output_mkpts, matches_result_info, matcher_info, ] # button callbacks button_run.click(fn=run_matching, inputs=inputs, outputs=outputs) # Reset images reset_outputs = [ match_setting_threshold, match_setting_max_features, detect_keypoints_threshold, matcher_list, input_image0, input_image1, input_image0, input_image1, match_image_src, output_mkpts, matches_result_info, matcher_info, ] button_reset.click(fn=ui_reset_state, inputs=inputs, outputs=reset_outputs) app.queue() app.launch(share=False) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--config_path", type=str, default="config.yaml", help="configuration file path" ) args = parser.parse_args() config = None run(config)