Worked on 2D viewer [no ci]

demo/README.md

@@ -40,6 +40,15 @@ of the predicted liver parenchyma 3D volume when finished processing.
 Analysis process can be monitored from the `Logs` tab next to the `Running` button
 in the Hugging Face `livermask` space.
 
+It is also possible to build the app as a docker image and deploy it. To do so follow these steps:
+
+```
+docker build -t livermask ..
+docker run -it -p 7860:7860 livermask
+```
+
+Then open `http://127.0.0.1:7860` in your favourite internet browser to view the demo.
+
 Natural future TODOs include:
 - [ ] Add gallery widget to enable scrolling through 2D slices
 - [ ] Render segmentation for individual 2D slices as overlays

demo/app.py

@@ -3,6 +3,8 @@ import subprocess as sp
 from skimage.measure import marching_cubes
 import nibabel as nib
 from nibabel.processing import resample_to_output
+import numpy as np
+import random
 
 
 def nifti_to_glb(path):
@@ -39,15 +41,138 @@ def load_mesh(mesh_file_name):
     return "./prediction.obj"
 
 
+def setup_gallery(data_path, pred_path):
+    image = nib.load(data_path)
+    resampled = resample_to_output(image, [1, 1, 1], order=1)
+    data = resampled.get_fdata().astype("uint8")
+
+    image = nib.load(pred_path)
+    resampled = resample_to_output(image, [1, 1, 1], order=0)
+    pred = resampled.get_fdata().astype("uint8")
+
+
+def load_ct_to_numpy(data_path):
+    if type(data_path) != str:
+        data_path = data_path.name
+
+    image = nib.load(data_path)
+    data = image.get_fdata()
+
+    data = np.rot90(data, k=1, axes=(0, 1))
+
+    data[data < -150] = -150
+    data[data > 250] = 250
+
+    data = data - np.amin(data)
+    data = data / np.amax(data) * 255
+    data = data.astype("uint8")
+
+    print(data.shape)
+    return [data[..., i] for i in range(data.shape[-1])]
+
+
+def upload_file(file):
+    return file.name
+
+#def select_section(evt: gr.SelectData):
+#    return section_labels[evt.index]
+
+
 if __name__ == "__main__":
     print("Launching demo...")
-    demo = gr.Interface(
-        fn=load_mesh,
-        inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
-        outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"),
-        title="livermask: Automatic Liver Parenchyma segmentation in CT",
-        description="Using pretrained deep learning model trained on the LiTS17 dataset",
-    )
+    with gr.Blocks() as demo:
+        """
+        with gr.Blocks() as demo:
+            with gr.Row():
+                text1 = gr.Textbox(label="t1")
+                slider2 = gr.Textbox(label="slide")
+                drop3 = gr.Dropdown(["a", "b", "c"], label="d3")
+            with gr.Row():
+                with gr.Column(scale=1, min_width=600):
+                    text1 = gr.Textbox(label="prompt 1")
+                    text2 = gr.Textbox(label="prompt 2")
+                    inbtw = gr.Button("Between")
+                    text4 = gr.Textbox(label="prompt 1")
+                    text5 = gr.Textbox(label="prompt 2")
+                with gr.Column(scale=2, min_width=600):
+                    img1 = gr.Image("images/cheetah.jpg")
+                    btn = gr.Button("Go").style(full_width=True)
+        
+        greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1"))
+        greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2"))
+        demo = gr.Parallel(greeter_1, greeter_2)
+
+        volume_renderer = gr.Interface(
+            fn=load_mesh,
+            inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
+            outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"),
+            title="livermask: Automatic Liver Parenchyma segmentation in CT",
+            description="Using pretrained deep learning model trained on the LiTS17 dataset",
+        )
+        """
+
+        with gr.Row():
+            # file_output = gr.File()
+            upload_button = gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single")
+            # upload_button.upload(upload_file, upload_button, file_output)
+
+            #select_btn = gr.Button("Run analysis")
+            #select_btn.click(fn=upload_file, inputs=upload_button, outputs=output, api_name="Analysis")
+        
+            #upload_button.click(section, [img_input, num_boxes, num_segments], img_output)
+        
+        #print("file output:", file_output)
+
+        images = load_ct_to_numpy("./test-volume.nii")
+
+        def variable_outputs(k):
+            k = int(k) - 1
+            out = [gr.AnnotatedImage.update(visible=False)] * len(images)
+            out[k] = gr.AnnotatedImage.update(visible=True)
+            return out
+        
+        def section(img, num_segments):
+            sections = []
+            for b in range(num_segments):
+                x = random.randint(0, img.shape[1])
+                y = random.randint(0, img.shape[0])
+                r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
+                mask = np.zeros(img.shape[:2])
+                for i in range(img.shape[0]):
+                    for j in range(img.shape[1]):
+                        dist_square = (i - y) ** 2 + (j - x) ** 2
+                        if dist_square < r**2:
+                            mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
+                sections.append((mask, "parenchyma"))
+            return (img, sections)
+        
+        with gr.Row():
+            s = gr.Slider(1, len(images), value=1, step=1, label="Which 2D slice to show")
+        
+        with gr.Row():
+            with gr.Box():
+                images_boxes = []
+                for i, image in enumerate(images):
+                    visibility = True if i == 1 else False  # only first slide visible - change slide through slider
+                    t = gr.AnnotatedImage(value=section(image, 1), visible=visibility).style(color_map={"parenchyma": "#ffae00"}, width=image.shape[1])
+                    images_boxes.append(t)
+
+                s.change(variable_outputs, s, images_boxes)
+
+        
+        #upload_button.upload(upload_file, upload_button, file_output)
+        
+        #section_btn.click(section, [images[40], num_boxes, num_segments], img_output)
+        #ct_images.upload(section, [images[40], num_boxes, num_segments], img_output)
+
+        #demo = gr.Interface(
+        #    fn=load_ct_to_numpy,
+        #    inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
+        #    outputs=gr.Gallery(label="CT slices").style(columns=[4], rows=[4], object_fit="contain", height="auto"),
+        #    title="livermask: Automatic Liver Parenchyma segmentation in CT",
+        #    description="Using pretrained deep learning model trained on the LiTS17 dataset",
+        #)
+
     # sharing app publicly -> share=True: https://gradio.app/sharing-your-app/
     # inference times > 60 seconds -> need queue(): https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
     demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=True)