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import gradio as gr
import cv2
import requests
import os
import tensorflow as tf
import numpy as np
file_urls = [
'https://drive.google.com/file/d/1dxNKawyagazR9TbxT6Fbla0opLqkROFG/view?usp=sharing',
'https://drive.google.com/file/d/109cc23-4HEyH9hFwenJjY7ZEVXj1-ZjW/view?usp=sharing',
'https://drive.google.com/file/d/1GLdsfug6n1tohqSAPmIzvCWZZSYIaHXj/view?usp=sharing'
]
def download_file(url, save_name):
if not os.path.exists(save_name):
file = requests.get(url)
open(save_name, 'wb').write(file.content)
for i, url in enumerate(file_urls):
if 'mp4' in file_urls[i]:
download_file(
file_urls[i],
f"video.mp4"
)
else:
download_file(
file_urls[i],
f"image_{i}.jpg"
)
# Load the TensorFlow model
model_path = 'saved_model' # Replace with your TensorFlow model path
model = tf.saved_model.load(model_path)
# Placeholder values for input dimensions
input_width = 640 # Replace with the actual input width of your model
input_height = 640 # Replace with the actual input height of your model
def predict_with_tf_model(image):
# Preprocess the image
image = cv2.resize(image, (input_width, input_height))
image = np.expand_dims(image, axis=0)
image = image / 255.0
# Run inference
output = model(image)
# Post-process the output if needed
return output
def show_preds_image(image_path):
image = cv2.imread(image_path)
# Perform inference using the TensorFlow model
output = predict_with_tf_model(image)
# Post-process the output and draw on the image if needed
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
inputs_image = [
gr.Image(type="filepath", label="Input Image"),
]
outputs_image = [
gr.Image(type="numpy", label="Output Image"),
]
interface_image = gr.Interface(
fn=show_preds_image,
inputs=inputs_image,
outputs=outputs_image,
title="Gregg Detector",
)
interface_image.launch()
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