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923712f 1ad6817 923712f 6fe4884 923712f f5cf4f0 5cd6c64 42577d4 9c94ab4 42577d4 33cc645 42577d4 33cc645 42577d4 60d20e1 ddbb3f6 56974de | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | import streamlit as st
from streamlit_back_camera_input import back_camera_input
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from PIL import Image
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
def main():
st.title("Visual Saliency Estimator")
# Let the user upload an image
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Open the uploaded image and display it
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image", use_column_width=True)
if st.button("Compute"):
# Convert the image to a numpy array for processing
img_array = np.array(image)
# Compute the saliency map (replace this with actual computation)
saliency_map = compute_saliency(img_array)
# Display the saliency map
st.image(saliency_map, caption="Saliency Map", use_column_width=True)
if __name__ == "__main__":
main()
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