Upload 6 files (#1)

- Upload 6 files (4792ad8931ac2c0630332b275b58cb77e23bc263)
- Update app.py (4551826032cdb26b2c75b62ba0475c2c6e23af5b)
- Update requirements.txt (ac6852893d6075b22d895dacf24e62a09ace7ef1)


Co-authored-by: Kadir Nar <kadirnar@users.noreply.huggingface.co>

app.py

@@ -1,7 +1,90 @@
+from transformers import SegformerForSemanticSegmentation
+from transformers import SegformerImageProcessor
+from PIL import Image
 import gradio as gr  
+import numpy as np
+import random 
+import cv2
+import torch
 
 
+image_list = [
+    "data/1.png",
+    "data/2.png",
+    "data/3.png",
+    "data/4.png",
+]
 
-#examples=["example_Benign1.png" ,"example_Benign2.png", "example_Malign2.png"]
-gr.Interface.load("huggingface/deprem-ml/deprem_satellite_semantic_whu",
-                  title="deprem_satellite_semantic_whu",cache_examples=False).launch()
+model_path = ['deprem-ml/deprem_satellite_semantic_whu']
+
+def visualize_instance_seg_mask(mask):
+    # Initialize image with zeros with the image resolution
+    # of the segmentation mask and 3 channels
+    image = np.zeros((mask.shape[0], mask.shape[1], 3))
+
+    # Create labels
+    labels = np.unique(mask)
+    label2color = {
+        label: (
+            random.randint(0, 255),
+            random.randint(0, 255),
+            random.randint(0, 255),
+        )
+        for label in labels
+    }
+
+    for height in range(image.shape[0]):
+        for width in range(image.shape[1]):
+            image[height, width, :] = label2color[mask[height, width]]
+
+    image = image / 255
+    return image
+
+
+def Segformer_Segmentation(image_path, model_id): 
+    output_save = "output.png"
+    
+    test_image = Image.open(image_path)
+
+    model = SegformerForSemanticSegmentation.from_pretrained(model_id)
+    proccessor = SegformerImageProcessor(model_id)
+    
+    inputs = proccessor(images=test_image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        
+    result = proccessor.post_process_semantic_segmentation(outputs)[0]
+    result = np.array(result)
+    result = visualize_instance_seg_mask(result)
+    cv2.imwrite(output_save, result*255)
+
+    return image_path, output_save
+
+examples = [[image_list[0], "deprem-ml/deprem_satellite_semantic_whu"],
+            [image_list[1], "deprem-ml/deprem_satellite_semantic_whu"],
+            [image_list[2], "deprem-ml/deprem_satellite_semantic_whu"],
+            [image_list[3], "deprem-ml/deprem_satellite_semantic_whu"]]
+
+title = "Deprem ML - Segformer Semantic Segmentation"
+
+app = gr.Blocks()
+with app:
+    gr.HTML("<h1 style='text-align: center'>{}</h1>".format(title))    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("Video")
+            input_video = gr.Image(type='filepath')
+            model_id =  gr.Dropdown(value=model_path[0], choices=model_path)
+            input_video_button = gr.Button(value="Predict")    
+                    
+        with gr.Column():
+            output_orijinal_image = gr.Image(type='filepath')
+            
+        with gr.Column():
+            output_mask_image =  gr.Image(type='filepath')
+
+        
+    gr.Examples(examples, inputs=[input_video, model_id], outputs=[output_orijinal_image, output_mask_image], fn=Segformer_Segmentation, cache_examples=True)
+    input_video_button.click(Segformer_Segmentation, inputs=[input_video, model_id], outputs=[output_orijinal_image, output_mask_image])
+
+app.launch() 

requirements.txt

@@ -0,0 +1,7 @@
+gradio==3.18.0
+matplotlib==3.6.2
+numpy==1.24.2
+Pillow==9.4.0
+torch==1.12.1
+transformers==4.26.0
+opencv-python