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app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../main.ipynb.
+
+# %% auto 0
+__all__ = ['ORGAN', 'IMAGE_SIZE', 'MODEL_NAME', 'THRESHOLD', 'CODES', 'learn', 'title', 'description', 'examples',
+           'interpretation', 'demo', 'x_getter', 'y_getter', 'splitter', 'make3D', 'predict', 'infer',
+           'remove_small_segs', 'to_oberlay_image']
+
+# %% ../main.ipynb 1
+import numpy as np
+import pandas as pd
+import skimage
+from fastai.vision.all import *
+import segmentation_models_pytorch as smp
+
+import gradio as gr
+
+# %% ../main.ipynb 2
+ORGAN = "kidney"
+IMAGE_SIZE = 512
+MODEL_NAME = "unetpp_b4_th60_d9414.pkl"
+THRESHOLD = float(MODEL_NAME.split("_")[2][2:]) / 100.
+CODES = ["Background", "FTU"] # FTU = functional tissue unit
+
+# %% ../main.ipynb 3
+def x_getter(r): return r["fnames"]
+def y_getter(r): 
+    rle = r["rle"]
+    shape = (int(r["img_height"]), int(r["img_width"]))
+    return rle_decode(rle, shape).T
+def splitter(a): 
+    enc_params = L(model.encoder.parameters())
+    dec_params = L(model.decoder.parameters())
+    sg_params = L(model.segmentation_head.parameters())
+    untrained_params = L([*dec_params, *sg_params])
+    return L([enc_params, untrained_params])
+
+# %% ../main.ipynb 4
+learn = load_learner(MODEL_NAME)
+
+# %% ../main.ipynb 5
+def make3D(t: np.array) -> np.array:
+    t = np.expand_dims(t, axis=2)
+    t = np.concatenate((t,t,t), axis=2)
+    return t
+
+def predict(fn, cutoff_area=200):
+    data = infer(fn)
+    data = remove_small_segs(data, cutoff_area=cutoff_area)
+    return to_oberlay_image(data), data["df"]
+
+def infer(fn):
+    img = PILImage.create(fn)
+    tf_img,_,_,preds = learn.predict(img, with_input=True)
+    mask = (F.softmax(preds.float(), dim=0)>THRESHOLD).int()[1]
+    mask = np.array(mask, dtype=np.uint8)
+    resized_image = Image.fromarray(tf_img.numpy().transpose(1, 2, 0).astype(np.uint8)).resize(img.shape)
+    resized_image = np.array(resized_image)
+    return {
+        "tf_image": tf_img.numpy().transpose(1, 2, 0).astype(np.uint8),
+        "tf_mask": mask
+    }
+
+def remove_small_segs(data, cutoff_area=250):
+    labeled_mask = skimage.measure.label(data["tf_mask"])
+    props = skimage.measure.regionprops(labeled_mask)
+    df = {"Glomerulus":[], "Area (in px)":[]}
+    for i, prop in enumerate(props):
+        if prop.area < cutoff_area: 
+            labeled_mask[labeled_mask==i+1] = 0
+            continue
+        df["Glomerulus"].append(len(df["Glomerulus"]) + 1)        
+        df["Area (in px)"].append(prop.area)
+    labeled_mask[labeled_mask>0] = 1
+    data["tf_mask"] = labeled_mask.astype(np.uint8)
+    data["df"] = pd.DataFrame(df)
+    return data
+
+def to_oberlay_image(data):
+    img, msk = data["tf_image"], data["tf_mask"]
+    msk_im = np.zeros_like(img)
+    # rgb code: 255, 80, 80
+    msk_im[:,:,0] = 255
+    msk_im[:,:,1] = 80
+    msk_im[:,:,2] = 80
+    img = Image.fromarray(img).convert("RGBA")
+    msk_im = Image.fromarray(msk_im).convert("RGBA")
+    msk = Image.fromarray((msk*255*0.5).astype(np.uint8))
+
+    img.paste(msk_im, (0, 0), msk, )
+    return img
+
+# %% ../main.ipynb 6
+title = "Glomerulus Segmentation"
+description = """
+A web app, that segments glomeruli in histologic kidney slices!
+
+The model deployed here is a [UnetPlusPlus](https://arxiv.org/abs/1807.10165) with an [efficientnet-b4](https://arxiv.org/abs/1905.11946) encoder from the [segmentation_models_pytorch](https://github.com/qubvel/segmentation_models.pytorch) library.
+
+The provided example images are random subset of kidney slices from the [Human Protein Atlas](https://www.proteinatlas.org/). These have been collected separately from model training and have neither been part of the training nor test set.
+
+Find the corresponding blog post [here](https://www.fast.ai/).
+"""
+#article="<p style='text-align: center'><a href='Blog post URL' target='_blank'>Blog post</a></p>"
+examples = [str(p) for p in get_image_files("example_images")]
+interpretation='default'
+
+# %% ../main.ipynb 7
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.components.Image(shape=(IMAGE_SIZE, IMAGE_SIZE)),
+    outputs=[gr.components.Image(), gr.components.DataFrame()],
+    title=title,
+    description=description,
+    examples=examples,
+    interpretation=interpretation,
+)
+
+# %% ../main.ipynb 9
+demo.launch()