project

.gitignore

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+env/

app.py

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+import gradio as gr
+import glob
+import os
+import cv2
+import glob
+from models.model import Model
+#from evaluation import evaluation
+datasets = ["HRF","DR-HAGIS","OVRS","ISBI2016_ISIC", "CHASEDB","KEVASIR-SEG"]
+models = ["UNET","SA-UNET", "ATT-UNET","SAM", "SAM-ZERO-SHOT"]
+
+
+
+
+def default_mask():
+    return None
+def default_metrics():
+    return {}
+def on_select_gallery(dataset: gr.SelectData):
+    path = glob.glob(os.path.join("datasets",dataset.value[0],"test/images/*"))
+    return gr.update(visible=True, value = path)
+def on_select_dataset_path(dataset: gr.SelectData):
+    path = glob.glob(os.path.join("datasets",dataset.value[0],"test/images/*"))
+    return  path
+def on_select_gallery_label(dataset: gr.SelectData):
+    path =dataset.value[0]
+    return gr.update(visible=True, value = "## "+path+" Dataset")
+def on_select_gallery_set_mask_path(value: gr.SelectData,path,dataset):
+    image_path = path[int(value.index)]
+    return os.path.join("datasets", dataset,"test","masks",os.path.basename(image_path))
+def set_index(value: gr.SelectData):
+    return str(value.index)
+def set_value(value: gr.SelectData):
+    return str(value.value)[2:-2]
+def segment(dataset, model, image_index, path):
+    return gr.update(visible= True)
+def segment_(dataset, model, image_index,path):
+    image_path = path[int(image_index)]
+    mask_path = os.path.join("datasets", dataset,"test","masks",os.path.basename(image_path))
+    return model.predict(image_path, mask_path)
+def on_select_model(model: gr.SelectData,dataset):
+    return Model(model.value, dataset)
+
+def update_model_dataset(dataset : gr.SelectData , model):
+    return gr.update(value = model.set_dataset(dataset.value[0]))
+def update_model_name(model_name : gr.SelectData, model):
+    return gr.update(value = model.set_model(model_name.value))
+def update_dropdown_labels(dataset : gr.SelectData):
+    choises = glob.glob(f"models/models_checkpoints/{dataset.value[0]}/*pth")
+    choises = list(map(lambda value: value.split("/")[-1].split('.')[0], choises))
+    return gr.Dropdown.update(choices = choises, interactive=True, label = "Models")
+
+def test(model):
+    return gr.update(value=model.evaluation())
+
+
+
+with gr.Blocks() as demo:
+    ## States
+    model_build = gr.State()
+    mask_p_state  = gr.State()
+    #models_state = gr.State([])
+    dataset_state = gr.State("ISBI2016_ISIC")
+    path = glob.glob(os.path.join("datasets",dataset_state.value,"test/images/*"))
+    path = gr.State(value=path)
+    model_state  = gr.State(value = Model("UNET", dataset_state.value))
+    demo_header = gr.Markdown(value="# A Comparative Analysis of State-of-the-Art Deep learning Models for Medical Image")
+    image_index = gr.Markdown(visible = False)
+    mask_y_state = gr.State()
+
+    dataset = gr.Dataset(label="Datasets",components = [gr.Textbox(visible= False)], samples = list(map(lambda item: [item], datasets)),)
+    model = gr.Dropdown(interactive=False, label = "Models",value=lambda : "UNET")
+    dataset.select(update_dropdown_labels,None,outputs = [model])
+    model.select(update_model_name,[model_state] , [model_state])
+    dataset_label = gr.Markdown()
+    gallery = gr.Gallery(value = path.value,visible=True)
+    dataset.select(on_select_gallery,None,outputs = [gallery])
+    dataset.select(on_select_gallery_label,None,outputs = [dataset_label])
+    dataset.select(set_value,None,outputs = [dataset_state])
+    dataset.select(update_model_dataset,[model_state],outputs = [model_state])
+    dataset.select(on_select_dataset_path,None,outputs = [path])
+    segement_btn = gr.Button(value = "Segment")
+    dataset_label = gr.Markdown(value="# Result")
+    gallery.select(set_index, None,outputs= [image_index])
+    #mask_y_image = gr.Image(scale=2)
+    row = gr.Row(visible=False)
+    segement_btn.click(segment, [dataset_state, model_state, image_index, path], [row])
+    with row:
+        mask_p_image = gr.Image(scale=2)
+        mask_y_image = gr.Image(scale=2)
+        metrics_label = gr.Label(label = "Metrics",inputs=[mask_p_image])
+        mask_p_image.change(test, inputs=[model_state],outputs = [metrics_label])
+    gallery.select(on_select_gallery_set_mask_path, [path, dataset_state],outputs= [mask_y_state])
+    gallery.select(on_select_gallery_set_mask_path, [path, dataset_state],outputs= [mask_y_image])
+    segement_btn.click(segment_, [dataset_state, model_state, image_index, path], [mask_p_image])
+    dataset.select(default_mask,None, outputs = [mask_p_image])
+    dataset.select(default_metrics, None,outputs= [metrics_label])
+    gallery.select(default_metrics, None,outputs= [metrics_label])
+    gallery.select(default_mask, None,outputs= [mask_p_image])
+demo.launch()

requirements.txt

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+albumentations==1.3.1
+gradio==3.44.4
+imageio==2.31.3
+numpy==1.25.2
+opencv_python==4.8.0.76
+opencv_python_headless==4.8.0.76
+Pillow==10.0.1
+pycocotools==2.0.7
+python_box==7.1.1
+scikit_learn==1.3.0
+scipy==1.11.2
+torch==2.0.1+cpu
+torchvision==0.15.2+cpu
+transformers==4.33.0