Add first app

app.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+from scipy.fftpack import dct
+from datasets import load_dataset
+from PIL import Image
+from multiprocessing import cpu_count
+
+
+def perceptual_hash_color(image):
+    image = image.convert("RGB")  # Convert to grayscale
+    image = image.resize((32, 32), Image.ANTIALIAS)  # Resize to 32x32
+    image_array = np.asarray(image)  # Convert to numpy array
+    hashes = []
+    for i in range(3):
+        channel = image_array[:, :, i]
+        dct_coef = dct(dct(channel, axis=0), axis=1)  # Compute DCT
+        dct_reduced_coef = dct_coef[:8, :8]  # Retain top-left 8x8 DCT coefficients
+        # Median of DCT coefficients excluding the DC term (0th term)
+        median_coef_val = np.median(np.ndarray.flatten(dct_reduced_coef)[1:])
+        # Mask of all coefficients greater than median of coefficients
+        hashes.append((dct_reduced_coef >= median_coef_val).flatten() * 1)
+    return np.concatenate(hashes)
+
+def hamming_distance(array_1, array_2):
+    return len([1 for el_1, el_2 in zip(array_1, array_2) if el_1 != el_2])
+
+def search_closest_examples(hash_refs, img_dataset):
+    distances = []
+    for hash_ref in hash_refs:
+        distances.extend([hamming_distance(hash_ref, img_dataset[idx]["hash"]) for idx in range(img_dataset.num_rows)])
+    closests = [i.item() % len(img_dataset) for i in np.argsort(distances)[:9]]
+    return closests, [distances[c] for c in closests]
+
+def find_closest_images(images, img_dataset):
+    if not isinstance(images, (list, tuple)):
+        images = [images]
+    hashes =  [perceptual_hash_color(img) for img in images]
+    closest_idx, distances = search_closest_examples(hashes, img_dataset)
+    return closest_idx, distances
+
+def compute_hash_from_image(img):
+    img = img.convert("L")  # Convert to grayscale
+    img = img.resize((32, 32), Image.ANTIALIAS)  # Resize to 32x32
+    img_array = np.asarray(img)  # Convert to numpy array
+    dct_coef = dct(dct(img_array, axis=0), axis=1)  # Compute DCT
+    dct_reduced_coef = dct_coef[:8, :8]  # Retain top-left 8x8 DCT coefficients
+    # Median of DCT coefficients excluding the DC term (0th term)
+    median_coef_val = np.median(np.ndarray.flatten(dct_reduced_coef)[1:])
+    # Mask of all coefficients greater than median of coefficients
+    hash = (dct_reduced_coef >= median_coef_val).flatten() * 1
+    return hash
+
+
+def process_dataset(dataset_name, dataset_split, dataset_column_image):
+    img_dataset = load_dataset(dataset_name)[dataset_split]
+
+    def add_hash(example):
+        example["hash"] = perceptual_hash_color(example[dataset_column_image])
+        return example
+
+    # Compute hash of every image in the dataset
+    img_dataset = img_dataset.map(add_hash, num_proc=4)
+    return img_dataset
+    
+
+def compute(dataset_name, dataset_split, dataset_column_image, img):
+    img_dataset = process_dataset(dataset_name, dataset_split, dataset_column_image)
+    closest_idx, distances = find_closest_images(img, img_dataset)
+    return [img_dataset[i] for i in closest_idx]
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Find if your images are in a public dataset!")
+    with gr.Row():
+        with gr.Column(scale=1, min_width=600):
+            dataset_name = gr.Textbox(label="Enter the name of a dataset containing images")
+            dataset_split = gr.Textbox(label="Enter the split of this dataset to consider")
+            dataset_column_image = gr.Textbox(label="Enter the name of the column of this dataset that contains images")
+            img = gr.Image(label="Input your image that will be compared against images of the dataset", type="pil")
+            btn = gr.Button("Find").style(full_width=True)
+
+        with gr.Column(scale=2, min_width=600):
+            gallery_similar = gr.Gallery(label="similar images")
+        
+    event = btn.click(compute, [dataset_name, dataset_split, dataset_column_image, img], gallery_similar)
+
+
+demo.launch()