update

README.md

@@ -1,14 +1,13 @@
 ---
-title: ImageNet Hard Browser
-emoji: 🐨
+title: ImageNet-Hard Browser
+emoji: 🔍
 colorFrom: indigo
 colorTo: gray
-sdk: docker
-sdk_version: 3.1.1
+sdk: gradio
+sdk_version: 4.9.0
 app_file: app.py
 pinned: false
 license: mit
-app_port: 8888
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,47 +1,189 @@
 import os
 from io import BytesIO
 from multiprocessing import Pool, cpu_count
-import fiftyone as fo
 from datasets import load_dataset
 from PIL import Image
+import gradio as gr
+import pandas as pd
 
-# Load the dataset
-imagenet_hard_dataset = load_dataset('taesiri/imagenet-hard', split='validation')
-os.makedirs("dataset", exist_ok=True)
+imagenet_hard_dataset = load_dataset("taesiri/imagenet-hard", split="validation")
+THUMBNAIL_PATH = "dataset/thumbnails"
+os.makedirs(THUMBNAIL_PATH, exist_ok=True)
+
+max_size = (480, 480)
+
+all_origins = set()
+all_labels = set()
+dataset_df = None
 
 
 def process_image(i):
+    global all_origins
     image = imagenet_hard_dataset[i]["image"].convert("RGB")
-    image_path = f"dataset/{i}.JPEG"
-    image.save(image_path, "JPEG", quality=80)
+    url_prefix = "https://imagenet-hard.taesiri.ai/"
+
+    origin = imagenet_hard_dataset[i]["origin"]
+    label = imagenet_hard_dataset[i]["english_label"]
+
+    save_path = os.path.join(THUMBNAIL_PATH, origin)
+    # make sure the folder exists
+    os.makedirs(save_path, exist_ok=True)
+    image_path = os.path.join(save_path, f"{i}.jpg")
+
+    image.thumbnail(max_size, Image.LANCZOS)
+
+    image.save(image_path, "JPEG", quality=100)
+
+    url = url_prefix + image_path
+
     return {
-        "file_path": image_path,
-        "labels": imagenet_hard_dataset[i]["english_label"],
+        "preview": url,
+        "filepath": image_path,
         "origin": imagenet_hard_dataset[i]["origin"],
+        "labels": imagenet_hard_dataset[i]["english_label"],
     }
 
 
-def create_fiftyone_sample(sample):
-   origin_label = fo.Classification(label=str(sample["origin"]))
-   english_label = fo.Classification(label=str(sample["labels"]))
-   return fo.Sample(
-       filepath=sample["file_path"],
-       labels=fo.Classifications(classifications=[origin_label, english_label]),
-   )
+# PREPROCESSING
+if os.path.exists("dataset.pkl"):
+    dataset_df = pd.read_pickle("dataset.pkl")
+    all_origins = set(dataset_df["origin"])
+    all_labels = set().union(*dataset_df["labels"])
+else:
+    with Pool(cpu_count()) as pool:
+        samples_data = pool.map(process_image, range(len(imagenet_hard_dataset)))
+        dataset_df = pd.DataFrame(samples_data)
+        print(dataset_df)
+        all_origins = set(dataset_df["origin"])
+        all_labels = set().union(*dataset_df["labels"])
+        # save dataframe on disk
+        dataset_df.to_csv("dataset.csv")
+        dataset_df.to_pickle("dataset.pkl")
+
 
+def get_slice(origin, label):
+    global dataset_df
 
+    if not origin and not label:
+        filtered_df = dataset_df
+    else:
+        filtered_df = dataset_df[
+            (dataset_df["origin"] == origin if origin else True)
+            & (dataset_df["labels"].apply(lambda x: label in x) if label else True)
+        ]
 
-if __name__ == "__main__":
-    # Process images in parallel and get the list of images with their labels
-    with Pool(cpu_count()) as pool:
-        samples_data = pool.map(process_image, range(len(imagenet_hard_dataset)))
+    max_value = len(filtered_df) // 16
+
+    returned_values = []
+
+    start_index = 0
+    end_index = start_index + 16
+
+    slice_df = filtered_df.iloc[start_index:end_index]
+
+    for row in slice_df.itertuples():
+        returned_values.append(gr.update(value=row.preview))
+        returned_values.append(gr.update(value=row.origin))
+        returned_values.append(gr.update(value=row.labels))
+
+    if len(returned_values) < 48:
+        returned_values.extend([None] * (48 - len(returned_values)))
+
+    filtered_df = gr.Dataframe(filtered_df, datatype="markdown")
+    return filtered_df, gr.update(maximum=max_value, value=0), *returned_values
+
+
+def reset_filters_fn():
+    return gr.update(value=None), gr.update(value=None)
+
+
+def make_grid(grid_size):
+    list_of_components = []
+
+    with gr.Row():
+        for row_counter in range(grid_size[0]):
+            with gr.Column():
+                for col_counter in range(grid_size[1]):
+                    item_image = gr.Image()
+                    with gr.Accordion("Click for details", open=False):
+                        item_source = gr.Textbox(label="Source Dataset")
+                        item_labels = gr.Textbox(label="Labels")
+
+                    list_of_components.append(item_image)
+                    list_of_components.append(item_source)
+                    list_of_components.append(item_labels)
+
+    return list_of_components
+
+
+def slider_upadte(slider, df):
+    returned_values = []
+
+    start_index = (slider) * 16
+    end_index = start_index + 16
+
+    slice_df = df.iloc[start_index:end_index]
+
+    for row in slice_df.itertuples():
+        returned_values.append(gr.update(value=row.preview))
+        returned_values.append(gr.update(value=row.origin))
+        returned_values.append(gr.update(value=row.labels))
+
+    if len(returned_values) < 48:
+        returned_values.extend([None] * (48 - len(returned_values)))
+
+    return returned_values
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# ImageNet-Hard Browser")
+    # add link to home page and dataset
+    gr.HTML("")
+    gr.HTML()
+    gr.HTML(
+        """
+    <center>
+        <span style="font-size: 14px; vertical-align: middle;">
+            <a href='https://zoom.taesiri.ai/'>Project Home Page</a> &nbsp;|&nbsp;
+            <a href='https://huggingface.co/datasets/taesiri/imagenet-hard'>Dataset</a>
+        </span>
+    </center>
+    """
+    )
+
+    with gr.Row():
+        origin_dropdown = gr.Dropdown(all_origins, label="Origin")
+        label_dropdown = gr.Dropdown(all_labels, label="Label")
+    with gr.Row():
+        show_btn = gr.Button("Show")
+        reset_filters = gr.Button("Reset Filters")
+
+    preview_dataframe = gr.Dataframe(height=500, visible=False)
+
+    gr.Markdown("## Preview")
+
+    maximum_vale = len(dataset_df) // 16
+
+    preview_slider = gr.Slider(minimum=1, maximum=maximum_vale, step=1, value=1)
+    all_components = make_grid((4, 4))
+
+    show_btn.click(
+        fn=get_slice,
+        inputs=[origin_dropdown, label_dropdown],
+        outputs=[preview_dataframe, preview_slider, *all_components],
+    )
+
+    reset_filters.click(
+        fn=reset_filters_fn,
+        inputs=[],
+        outputs=[origin_dropdown, label_dropdown],
+    )
 
-    # Create a FiftyOne dataset
-    dataset = fo.Dataset(name="imagenet-hard")
+    preview_slider.change(
+        fn=slider_upadte,
+        inputs=[preview_slider, preview_dataframe],
+        outputs=[*all_components],
+    )
 
-    # Add images and labels to the FiftyOne dataset
-    samples = [create_fiftyone_sample(sample_data) for sample_data in samples_data]
-    dataset.add_samples(samples)
 
-    session = fo.launch_app(dataset, port=8888, remote=True, address="0.0.0.0")
-    session.wait()
+demo.launch()

requirements.txt

@@ -1,5 +1,5 @@
-fiftyone
 transformers
 datasets
 tqdm
-numpy
+numpy
+pandas