addedo images

app.py

@@ -1,6 +1,5 @@
-import os, torch
+import torch, uuid
 import gradio as gr
-from PIL import Image
 
 from utils.imageHandling import hfImageToTensor, preprocessing, postprocessing, loadPreloadedImages
 from model.modelLoading import loadBiSeNet, loadBiSeNetV2
@@ -9,6 +8,7 @@ from model.modelLoading import loadBiSeNet, loadBiSeNetV2
 ## %% CONSTANTS
 gta_image_dir = "./preloadedImages/GTAV"
 city_image_dir = "./preloadedImages/cityScapes"
+turin_image_dir = "./preloadedImages/turin"
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 
@@ -18,9 +18,13 @@ MODELS = {
 }
 
 
-def load_example(index:int=0, useGta:bool=True)-> gr.Image:
-    example_img = loadPreloadedImages(gta_image_dir if useGta else city_image_dir)
-    return gr.update(value=example_img[index if 0 <= index < len(example_img) else 0], visible=True)
+image_list = loadPreloadedImages(gta_image_dir, city_image_dir, turin_image_dir)
+uuid_to_path = dict()
+
+for i in range(len(image_list)):
+    uid = str(uuid.uuid5(uuid.NAMESPACE_URL, image_list[i][1]))  
+    uuid_to_path[uid] = image_list[i][1]
+    image_list[i][1] = uid  
 
 
 # %% prediction on an image
@@ -81,35 +85,14 @@ with gr.Blocks(title="Semantic Segmentation Predictors") as demo:
             error_text = gr.Markdown("", visible=False)
 
     with gr.Row():
-        gr.Markdown("## Preloaded GTA V images to be used for testing the model")
-    with gr.Row():
-        gta_gallery = gr.Gallery(
-            value=loadPreloadedImages(gta_image_dir),
-            label="GTA V Examples",
-            show_label=False,
-            columns=5,
-            type="index",
-            rows=1,
-            height=200,
-            allow_preview=False
-        )
-
-    with gr.Row():
-        gr.Markdown("## Preloaded Cityscapes images to be used for testing the model")
+        gr.Markdown("## Preloaded images to be used for testing the model")
+        gr.Markdown("You can use images from the Grand Theft Auto V video game, the Cityscapes dataset or even from Turin")
     with gr.Row():
-        city_gallery = gr.Gallery(
-            value=loadPreloadedImages(city_image_dir),
-            label="Cityscapes Examples",
-            show_label=False,
-            columns=5,
-            type="index",
-            rows=1,
-            height=256,
-            allow_preview=False
+        image_gallery = gr.Gallery(value=image_list, label="Preloaded Examples",
+            type="value",  columns=5,  rows=4,
+            height=1200, allow_preview=False
         )
 
-    gta_gallery.select(fn=lambda i: load_example(i, True), inputs=[], outputs=image_input)
-    city_gallery.select(fn=lambda i: load_example(i, False), inputs=[], outputs=image_input)
 
     submit_btn.click(
         fn=run_prediction,

utils/imageHandling.py

@@ -85,14 +85,15 @@ def postprocessing(pred: torch.Tensor) -> torch.Tensor:
 
 
 # %% preloaded images
-def loadPreloadedImages(image_dir: str) -> list[Image.Image]:
+def loadPreloadedImages(*args:str) -> list[tuple[Image.Image, str]]:
     """
     Load preloaded images from a directory.
 
     Args:
-        image_dir (str): Path to the directory containing images.
+        args (str): Path to the directory containing images.
 
     Returns:
-        list[Image.Image]: List of loaded images.
+        images (list[tuple[Image.Image, str]]): List of loaded images with their original paths.
     """
-    return list(map(lambda f: Image.open(os.path.join(image_dir, f)).convert("RGB"), sorted([f for f in os.listdir(image_dir) if f.endswith(".png")])))
+    return sorted([(Image.open(os.path.join(imageDir, image)).convert("RGB"), os.path.join(imageDir, image))
+                    for imageDir in args for image in os.listdir(imageDir) if image.endswith([".png", ".jpg", "jpeg"])])