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app.py
CHANGED
@@ -91,12 +91,12 @@ def match(query_feat, pos_feat, LoweRatioTh=0.9):
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col = plt.get_cmap('tab10')
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def generate_matching_superfeatures(im1, im2,
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print('im1:', im1.size)
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print('im2:', im2.size)
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net = net_sfm
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if
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net = net_imagenet
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# dataset_ = ImgDataset(images=[im1, im2], imsize=1024)
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@@ -246,14 +246,6 @@ title = "Visualizing Super-features"
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description = "This is a visualization demo for the ICLR 2022 paper <b><a href='https://github.com/naver/fire' target='_blank'>Learning Super-Features for Image Retrieval</a></p></b>"
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article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>"
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# css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
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# css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
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# css = ".output_image, .input_image {hieght: 1000px !important}"
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css = ".input_image, .input_image {height: 600px !important; width: 600px !important;} "
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# css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
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iface = gr.Interface(
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fn=generate_matching_superfeatures,
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inputs=[
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@@ -261,11 +253,11 @@ iface = gr.Interface(
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# gr.inputs.Image(shape=(1024, 1024), type="pil", label="Second Image"),
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gr.inputs.Image(type="pil", label="First Image"),
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gr.inputs.Image(type="pil", label="Second Image"),
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gr.inputs.
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gr.inputs.Slider(minimum=0, maximum=6, step=1, default=2, label="Scale"),
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gr.inputs.Slider(minimum=0, maximum=255, step=25, default=150, label="Binarization Threshold"),
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gr.inputs.Checkbox(default=False, label="Show random matching SFs"),
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gr.inputs.Textbox(lines=1, default="", label="
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gr.inputs.Checkbox(default=True, label="Show only matching SFs"),
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],
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outputs=[
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@@ -278,11 +270,10 @@ iface = gr.Interface(
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layout="horizontal",
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description=description,
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article=article,
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css=css,
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examples=[
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["chateau_1.png", "chateau_2.png", 3, 150, '
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["anafi1.jpeg", "anafi2.jpeg", 4, 150, '
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["areopoli1.jpeg", "areopoli2.jpeg", 4, 150, '
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]
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)
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iface.launch(enable_queue=True)
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col = plt.get_cmap('tab10')
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def generate_matching_superfeatures(im1, im2, Imagenet_model=False, scale_id=6, threshold=50, random_mode=False, sf_ids='', only_matching=True):
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print('im1:', im1.size)
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print('im2:', im2.size)
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net = net_sfm
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if Imagenet_model:
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net = net_imagenet
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# dataset_ = ImgDataset(images=[im1, im2], imsize=1024)
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description = "This is a visualization demo for the ICLR 2022 paper <b><a href='https://github.com/naver/fire' target='_blank'>Learning Super-Features for Image Retrieval</a></p></b>"
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article = "<p style='text-align: center'><a href='https://github.com/naver/fire' target='_blank'>Original Github Repo</a></p>"
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iface = gr.Interface(
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fn=generate_matching_superfeatures,
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inputs=[
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# gr.inputs.Image(shape=(1024, 1024), type="pil", label="Second Image"),
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gr.inputs.Image(type="pil", label="First Image"),
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gr.inputs.Image(type="pil", label="Second Image"),
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gr.inputs.Checkbox(default=False, label="Model trained on ImageNet (Default: SfM-120k)"),
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gr.inputs.Slider(minimum=0, maximum=6, step=1, default=2, label="Scale"),
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gr.inputs.Slider(minimum=0, maximum=255, step=25, default=150, label="Binarization Threshold"),
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gr.inputs.Checkbox(default=False, label="Show random (matching) SFs"),
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gr.inputs.Textbox(lines=1, default="", label="...or show specific SF IDs:", optional=True),
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gr.inputs.Checkbox(default=True, label="Show only matching SFs"),
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],
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outputs=[
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layout="horizontal",
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description=description,
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article=article,
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examples=[
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["chateau_1.png", "chateau_2.png", False, 3, 150, True, '', True],
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["anafi1.jpeg", "anafi2.jpeg", False, 4, 150, True, '', True],
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["areopoli1.jpeg", "areopoli2.jpeg", False, 4, 150, True, '', True],
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]
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)
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iface.launch(enable_queue=True)
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