Spaces:
Runtime error
Runtime error
File size: 3,851 Bytes
62cf953 f18dab7 62cf953 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
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 on 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()
|