initial commit

README.md

@@ -1,10 +1,10 @@
 ---
-title: Saliency
+title: Visual Saliency Prediction
 emoji: ⚡
-colorFrom: indigo
+colorFrom: pink
 colorTo: blue
 sdk: gradio
-sdk_version: 4.20.1
+sdk_version: 4.26.0
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -0,0 +1,119 @@
+import gradio as gr
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+loaded_model = tf.saved_model.load("model/")
+loaded_model = loaded_model.signatures["serving_default"]
+
+
+def get_target_shape(original_shape):
+    original_aspect_ratio = original_shape[0] / original_shape[1]
+
+    square_mode = abs(original_aspect_ratio - 1.0)
+    landscape_mode = abs(original_aspect_ratio - 240 / 320)
+    portrait_mode = abs(original_aspect_ratio - 320 / 240)
+
+    best_mode = min(square_mode, landscape_mode, portrait_mode)
+
+    if best_mode == square_mode:
+        target_shape = (320, 320)
+    elif best_mode == landscape_mode:
+        target_shape = (240, 320)
+    else:
+        target_shape = (320, 240)
+
+    return target_shape
+
+
+def preprocess_input(input_image, target_shape):
+    input_tensor = tf.expand_dims(input_image, axis=0)
+
+    input_tensor = tf.image.resize(
+        input_tensor, target_shape, preserve_aspect_ratio=True
+    )
+
+    vertical_padding = target_shape[0] - input_tensor.shape[1]
+    horizontal_padding = target_shape[1] - input_tensor.shape[2]
+
+    vertical_padding_1 = vertical_padding // 2
+    vertical_padding_2 = vertical_padding - vertical_padding_1
+
+    horizontal_padding_1 = horizontal_padding // 2
+    horizontal_padding_2 = horizontal_padding - horizontal_padding_1
+
+    input_tensor = tf.pad(
+        input_tensor,
+        [
+            [0, 0],
+            [vertical_padding_1, vertical_padding_2],
+            [horizontal_padding_1, horizontal_padding_2],
+            [0, 0],
+        ],
+    )
+
+    return (
+        input_tensor,
+        [vertical_padding_1, vertical_padding_2],
+        [horizontal_padding_1, horizontal_padding_2],
+    )
+
+
+def postprocess_output(
+    output_tensor, vertical_padding, horizontal_padding, original_shape
+):
+    output_tensor = output_tensor[
+        :,
+        vertical_padding[0] : output_tensor.shape[1] - vertical_padding[1],
+        horizontal_padding[0] : output_tensor.shape[2] - horizontal_padding[1],
+        :,
+    ]
+
+    output_tensor = tf.image.resize(output_tensor, original_shape)
+
+    output_array = output_tensor.numpy().squeeze()
+    output_array = plt.cm.inferno(output_array)[..., :3]
+
+    return output_array
+
+
+def compute_saliency(input_image, alpha=0.65):
+    if input_image is not None:
+        original_shape = input_image.shape[:2]
+        target_shape = get_target_shape(original_shape)
+
+        input_tensor, vertical_padding, horizontal_padding = preprocess_input(
+            input_image, target_shape
+        )
+
+        saliency_map = loaded_model(input_tensor)["output"]
+
+        saliency_map = postprocess_output(
+            saliency_map, vertical_padding, horizontal_padding, original_shape
+        )
+
+        blended_image = alpha * saliency_map + (1 - alpha) * input_image / 255
+
+        return blended_image
+
+
+examples = [
+    "examples/kirsten-frank-o1sXiz_LU1A-unsplash.jpg",
+    "examples/oscar-fickel-F5ze5FkEu1g-unsplash.jpg",
+    "examples/ting-tian-_79ZJS8pV70-unsplash.jpg",
+    "examples/gina-domenique-LmrAUrHinqk-unsplash.jpg",
+    "examples/robby-mccullough-r05GkQBcaPM-unsplash.jpg",
+]
+
+demo = gr.Interface(
+    fn=compute_saliency,
+    inputs=gr.Image(label="Input Image"),
+    outputs=gr.Image(label="Saliency Map"),
+    examples=examples,
+    title="Visual Saliency Prediction",
+    description="A demo to predict where humans fixate an image using a deep learning model trained on eye movement data. Upload an image file, take a snapshot from your webcam, or paste an image from the clipboard to compute the saliency map.",
+    article="For more information on the model, check out [GitHub](https://github.com/alexanderkroner/saliency) and the corresponding [paper](https://www.sciencedirect.com/science/article/pii/S0893608020301660).",
+    allow_flagging="never",
+)
+
+if __name__ == "__main__":
+    demo.queue().launch()

model/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:803688d92e101794f6ced50ca08b747706e385e7b815f309344632def6e1609b
+size 99864092

requirements.txt

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+gradio==4.26.0
+matplotlib==3.8.4
+tensorflow==2.16.1