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Runtime error
Runtime error
cleaning up hardcoded aspects of code
Browse files- app.py +13 -32
- image_posterior.py +11 -16
app.py
CHANGED
@@ -16,43 +16,25 @@ from bayes.data_routines import get_dataset_by_name
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from bayes.models import *
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from image_posterior import create_gif
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parser = argparse.ArgumentParser()
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parser.add_argument("--cred_width", type=float, default=0.1)
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parser.add_argument("--save_loc", type=str, required=True)
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parser.add_argument("--n_top_segs", type=int, default=5)
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parser.add_argument("--n_gif_images", type=int, default=20)
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# app = flask.Flask(__name__, template_folder="./")
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IMAGE_NAME = "imagenet_diego"
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BLENHEIM_SPANIEL_CLASS = 156
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def get_image_data():
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"""Gets the image data and model."""
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return
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def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
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print("GRADIO INPUTS:", image_name, c_width, n_top, n_gif_imgs)
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# html = "<div style=\"background-image: url(./imagenet_diego.png); height: 400px; width: 400px;\"></div>"
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html = (
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"<div >"
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"<img src='file/diego.gif' alt='picture of dog'/>"
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+ "</div>"
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)
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return html
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cred_width = c_width
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n_top_segs = n_top
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n_gif_images = n_gif_imgs
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-
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# Unpack datax
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xtest = model_and_data["xtest"]
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@@ -60,6 +42,7 @@ def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
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segs = model_and_data["xtest_segs"]
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get_model = model_and_data["model"]
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label = model_and_data["label"]
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# Unpack instance and segments
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instance = xtest[0]
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@@ -88,7 +71,7 @@ def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
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l2=False)
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# Create the gif of the explanation
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return create_gif(rout['blr'], segments, instance, n_gif_images, n_top_segs)
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def image_mod(image):
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return image.rotate(45)
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@@ -96,8 +79,8 @@ def image_mod(image):
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if __name__ == "__main__":
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# gradio's image inputs look like this: <PIL.Image.Image image mode=RGB size=305x266 at 0x7F3D01C91FA0>
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# need to learn how to handle image inputs, or deal with file inputs or just file path strings
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inp = gr.inputs.Textbox(lines=1, placeholder="
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out = gr.outputs.HTML(label="Output
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iface = gr.Interface(
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segmentation_generation,
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@@ -108,8 +91,6 @@ if __name__ == "__main__":
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gr.inputs.Slider(minimum=10, maximum=50, step=1, default=20, label="n_gif_images", optional=False),
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],
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outputs=out,
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examples=[["
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)
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iface.launch(enable_queue
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# app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860)))
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from bayes.models import *
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from image_posterior import create_gif
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BLENHEIM_SPANIEL_CLASS = 156
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def get_image_data(image_name):
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"""Gets the image data and model."""
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if (image_name == "imagenet_diego.png"):
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image = get_dataset_by_name("imagenet_diego", get_label=False)
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model_and_data = process_imagenet_get_model(image)
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return image, model_and_data
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def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
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print("GRADIO INPUTS:", image_name, c_width, n_top, n_gif_imgs)
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cred_width = c_width
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n_top_segs = n_top
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n_gif_images = n_gif_imgs
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image, model_and_data = get_image_data(image_name)
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# Unpack datax
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xtest = model_and_data["xtest"]
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segs = model_and_data["xtest_segs"]
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get_model = model_and_data["model"]
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label = model_and_data["label"]
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print("LABEL:", label)
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# Unpack instance and segments
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instance = xtest[0]
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l2=False)
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# Create the gif of the explanation
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return create_gif(rout['blr'], image_name, segments, instance, n_gif_images, n_top_segs)
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def image_mod(image):
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return image.rotate(45)
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if __name__ == "__main__":
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# gradio's image inputs look like this: <PIL.Image.Image image mode=RGB size=305x266 at 0x7F3D01C91FA0>
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# need to learn how to handle image inputs, or deal with file inputs or just file path strings
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inp = gr.inputs.Textbox(lines=1, placeholder="Select an example from below", default="", label="Input Image Path", optional=False)
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out = gr.outputs.HTML(label="Output GIF")
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iface = gr.Interface(
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segmentation_generation,
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gr.inputs.Slider(minimum=10, maximum=50, step=1, default=20, label="n_gif_images", optional=False),
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],
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outputs=out,
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examples=[["imagenet_diego.png", 0.01, 7, 50]]
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)
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iface.launch(show_error=True, enable_queue=True)
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image_posterior.py
CHANGED
@@ -41,7 +41,7 @@ def fill_segmentation(values, segmentation, image, n_max=5):
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c_image[segmentation == i, c] = np.max(image)
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return c_image.astype(int), out.astype(int)
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def create_gif(explanation_blr, segments, image, n_images=20, n_max=5):
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"""Create the gif corresponding to the image explanation.
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Arguments:
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@@ -64,25 +64,20 @@ def create_gif(explanation_blr, segments, image, n_images=20, n_max=5):
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plt.imshow(c_image, alpha=0.3)
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paths.append(os.path.join(tmpdirname, f"{i}.png"))
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plt.savefig(paths[-1])
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print("CREATING
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# Save to gif
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# https://stackoverflow.com/questions/61716066/creating-an-animation-out-of-matplotlib-pngs
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# print(f"Saving gif to {save_loc}")
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ims = [imageio.imread(f) for f in paths]
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html = '''
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<div style='max-width:100%; max-height:360px; overflow:auto'>
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<video width="320" height="240" autoplay>
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<source src="./test.mp4" type=video/mp4>
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</video>
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</div>
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)'''
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return html
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# return imageio.mimwrite(imageio.RETURN_BYTES, ims)
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c_image[segmentation == i, c] = np.max(image)
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return c_image.astype(int), out.astype(int)
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def create_gif(explanation_blr, img_name, segments, image, n_images=20, n_max=5):
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"""Create the gif corresponding to the image explanation.
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Arguments:
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plt.imshow(c_image, alpha=0.3)
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paths.append(os.path.join(tmpdirname, f"{i}.png"))
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plt.savefig(paths[-1])
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print("CREATING GIF NOW")
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# Save to gif
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# https://stackoverflow.com/questions/61716066/creating-an-animation-out-of-matplotlib-pngs
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# print(f"Saving gif to {save_loc}")
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ims = [imageio.imread(f) for f in paths]
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imageio.mimwrite(f'{img_name}_explanation.gif', ims)
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html = (
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"<div >"
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f"<img src='file/{img_name}_explanation.gif' alt='explanation gif'/>"
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+ "</div>"
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)
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return html
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