Minor text changes.

app.py

@@ -1,9 +1,27 @@
 # 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']
+__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
@@ -18,77 +36,96 @@ import gradio as gr
 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
+THRESHOLD = float(MODEL_NAME.split("_")[2][2:]) / 100.0
+CODES = ["Background", "FTU"]  # FTU = functional tissue unit
+
 
 # %% ../main.ipynb 3
-def x_getter(r): return r["fnames"]
-def y_getter(r): 
+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(model): 
+
+
+def splitter(model):
     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)
+    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]
+    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 = 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
+        "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)":[]}
+    df = {"Glomerulus": [], "Area (in px)": []}
     for i, prop in enumerate(props):
-        if prop.area < cutoff_area: 
-            labeled_mask[labeled_mask==i+1] = 0
+        if prop.area < cutoff_area:
+            labeled_mask[labeled_mask == i + 1] = 0
             continue
-        df["Glomerulus"].append(len(df["Glomerulus"]) + 1)        
+        df["Glomerulus"].append(len(df["Glomerulus"]) + 1)
         df["Area (in px)"].append(prop.area)
-    labeled_mask[labeled_mask>0] = 1
+    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
+    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))
+    msk = Image.fromarray((msk * 255 * 0.5).astype(np.uint8))
 
-    img.paste(msk_im, (0, 0), msk, )
+    img.paste(
+        msk_im,
+        (0, 0),
+        msk,
+    )
     return img
 
+
 # %% ../main.ipynb 6
 title = "Glomerulus Segmentation"
 description = """
@@ -98,11 +135,11 @@ The model deployed here is a [UNet++](https://arxiv.org/abs/1807.10165) with an
 
 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, validation nor test set.
 
-See corresponding [blog post](https://fhatje.github.io/posts/glomseg/train_model.html).
+Here is my corresponding [blog post](https://fhatje.github.io/posts/glomseg/train_model.html).
 """
-#article="<p style='text-align: center'><a href='Blog post URL' target='_blank'>Blog post</a></p>"
+# 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'
+interpretation = "default"
 
 # %% ../main.ipynb 7
 demo = gr.Interface(
@@ -113,10 +150,10 @@ demo = gr.Interface(
     description=description,
     examples=examples,
     interpretation=interpretation,
-    # Fixes error when set to True: 
-    # https://github.com/gradio-app/gradio/pull/1949  
+    # Fixes error when set to True:
+    # https://github.com/gradio-app/gradio/pull/1949
     # but generated file names are too long
-    _api_mode=False
+    _api_mode=False,
 )
 
 # %% ../main.ipynb 9