Gregg detector initial commit

.gitignore

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+flagged/
+*.pt
+*.png
+*.jpg
+*.mkv
+gradio_cached_examples/

app.py

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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 = 'path/to/your/tensorflow_model.pb'  # 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="file", 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()

saved_model/fingerprint.pb

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+size 55

saved_model/saved_model.pb

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saved_model/variables/variables.data-00000-of-00001

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saved_model/variables/variables.index

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