Rename main.py to app.py

main.py → app.py

@@ -1,152 +1,155 @@
-import gradio as gr
-import time
-from video_processing import process_video
-from PIL import Image
-import matplotlib
-matplotlib.rcParams['figure.dpi'] = 500
-matplotlib.rcParams['savefig.dpi'] = 500
-
-def process_and_show_completion(video_input_path, anomaly_threshold_input, fps, progress=gr.Progress()):
-    try:
-        print("Starting video processing...")
-        results = process_video(video_input_path, anomaly_threshold_input, fps, progress=progress)
-        print("Video processing completed.")
-
-        if isinstance(results[0], str) and results[0].startswith("Error"):
-            print(f"Error occurred: {results[0]}")
-            return [results[0]] + [None] * 18
-
-        exec_time, results_summary, df, mse_embeddings, mse_posture, \
-            mse_plot_embeddings, mse_histogram_embeddings, \
-            mse_plot_posture, mse_histogram_posture, \
-            mse_heatmap_embeddings, mse_heatmap_posture, \
-            face_samples_frequent, face_samples_other, \
-            anomaly_faces_embeddings, anomaly_frames_posture_images, \
-            aligned_faces_folder, frames_folder, \
-            anomaly_sentences_features, anomaly_sentences_posture = results
-
-        anomaly_faces_embeddings_pil = [Image.fromarray(face) for face in anomaly_faces_embeddings]
-        anomaly_frames_posture_pil = [Image.fromarray(frame) for frame in anomaly_frames_posture_images]
-
-        face_samples_frequent = [Image.open(path) for path in face_samples_frequent]
-        face_samples_other = [Image.open(path) for path in face_samples_other]
-
-        anomaly_sentences_features, anomaly_sentences_posture = results[-2:]
-
-        # Format anomaly sentences output
-        sentences_features_output = format_anomaly_sentences(anomaly_sentences_features, "Facial Features")
-        sentences_posture_output = format_anomaly_sentences(anomaly_sentences_posture, "Body Posture")
-
-        output = [
-            exec_time, results_summary,
-            df, mse_embeddings, mse_posture,
-            mse_plot_embeddings, mse_plot_posture,
-            mse_histogram_embeddings, mse_histogram_posture,
-            mse_heatmap_embeddings, mse_heatmap_posture,
-            anomaly_faces_embeddings_pil, anomaly_frames_posture_pil,
-            face_samples_frequent, face_samples_other,
-            aligned_faces_folder, frames_folder,
-            mse_embeddings, mse_posture,
-            sentences_features_output, sentences_posture_output
-        ]
-
-        return output
-
-    except Exception as e:
-        error_message = f"An error occurred: {str(e)}"
-        print(error_message)
-        import traceback
-        traceback.print_exc()
-        return [error_message] + [None] * 20
-
-with gr.Blocks() as iface:
-    gr.Markdown("""
-    # Facial Expression and Body Language Anomaly Detection
-
-    This application analyzes videos to detect anomalies in facial features and body language. 
-    It processes the video frames to extract facial embeddings and body posture, 
-    then uses machine learning techniques to identify unusual patterns or deviations from the norm.
-
-    For more information, visit: [https://github.com/reab5555/Facial-Expression-Anomaly-Detection](https://github.com/reab5555/Facial-Expression-Anomaly-Detection)
-    """)
-
-    with gr.Row():
-        video_input = gr.Video()
-
-    anomaly_threshold = gr.Slider(minimum=1, maximum=5, step=0.1, value=3, label="Anomaly Detection Threshold")
-    fps_slider = gr.Slider(minimum=5, maximum=20, step=1, value=10, label="Frames Per Second")
-    process_btn = gr.Button("Detect Anomalies")
-    progress_bar = gr.Progress()
-    execution_time = gr.Number(label="Execution Time (seconds)")
-
-    with gr.Group(visible=False) as results_group:
-        results_text = gr.TextArea(label="Anomaly Detection Results", lines=4)
-
-        with gr.Tab("Facial Features"):
-            mse_features_plot = gr.Plot(label="MSE: Facial Features")
-            mse_features_hist = gr.Plot(label="MSE Distribution: Facial Features")
-            mse_features_heatmap = gr.Plot(label="MSE Heatmap: Facial Features")
-            anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2, height="auto")
-
-        with gr.Tab("Body Posture"):
-            mse_posture_plot = gr.Plot(label="MSE: Body Posture")
-            mse_posture_hist = gr.Plot(label="MSE Distribution: Body Posture")
-            mse_posture_heatmap = gr.Plot(label="MSE Heatmap: Body Posture")
-            anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2, height="auto")
-
-        with gr.Tab("Sentences"):
-            with gr.Row():
-                anomaly_sentences_features_output = gr.Textbox(label="Sentences before Facial Feature Anomalies",
-                                                               lines=10)
-                anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2,
-                                                     height="auto")
-
-            with gr.Row():
-                anomaly_sentences_posture_output = gr.Textbox(label="Sentences before Body Posture Anomalies", lines=10)
-                anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2,
-                                                    height="auto")
-
-        with gr.Tab("Face Samples"):
-            face_samples_most_frequent = gr.Gallery(label="Most Frequent Person Samples (Target)", columns=6, rows=2, height="auto")
-            face_samples_others = gr.Gallery(label="Other Persons Samples", columns=6, rows=1, height="auto")
-
-    df_store = gr.State()
-    mse_features_store = gr.State()
-    mse_posture_store = gr.State()
-    aligned_faces_folder_store = gr.State()
-    frames_folder_store = gr.State()
-    mse_heatmap_embeddings_store = gr.State()
-    mse_heatmap_posture_store = gr.State()
-
-    def format_anomaly_sentences(anomaly_sentences, anomaly_type):
-        output = f"Sentences before {anomaly_type} Anomalies:\n\n"
-        for anomaly_timecode, sentences in anomaly_sentences:
-            output += f"Anomaly at {anomaly_timecode}:\n"
-            for sentence_timecode, sentence in sentences:
-                output += f"  [{sentence_timecode}] {sentence}\n"
-            output += "\n"
-        return output
-
-    process_btn.click(
-        process_and_show_completion,
-        inputs=[video_input, anomaly_threshold, fps_slider],
-        outputs=[
-            execution_time, results_text, df_store,
-            mse_features_store, mse_posture_store,
-            mse_features_plot, mse_posture_plot,
-            mse_features_hist, mse_posture_hist,
-            mse_features_heatmap, mse_posture_heatmap,
-            anomaly_frames_features, anomaly_frames_posture,
-            face_samples_most_frequent, face_samples_others,
-            aligned_faces_folder_store, frames_folder_store,
-            mse_heatmap_embeddings_store, mse_heatmap_posture_store,
-            anomaly_sentences_features_output, anomaly_sentences_posture_output
-        ]
-    ).then(
-        lambda: gr.Group(visible=True),
-        inputs=None,
-        outputs=[results_group]
-    )
-
-if __name__ == "__main__":
+import gradio as gr
+import time
+from video_processing import process_video
+from PIL import Image
+import matplotlib
+import spaces
+matplotlib.rcParams['figure.dpi'] = 500
+matplotlib.rcParams['savefig.dpi'] = 500
+
+@spaces.GPU(duration=300)
+
+def process_and_show_completion(video_input_path, anomaly_threshold_input, fps, progress=gr.Progress()):
+    try:
+        print("Starting video processing...")
+        results = process_video(video_input_path, anomaly_threshold_input, fps, progress=progress)
+        print("Video processing completed.")
+
+        if isinstance(results[0], str) and results[0].startswith("Error"):
+            print(f"Error occurred: {results[0]}")
+            return [results[0]] + [None] * 18
+
+        exec_time, results_summary, df, mse_embeddings, mse_posture, \
+            mse_plot_embeddings, mse_histogram_embeddings, \
+            mse_plot_posture, mse_histogram_posture, \
+            mse_heatmap_embeddings, mse_heatmap_posture, \
+            face_samples_frequent, face_samples_other, \
+            anomaly_faces_embeddings, anomaly_frames_posture_images, \
+            aligned_faces_folder, frames_folder, \
+            anomaly_sentences_features, anomaly_sentences_posture = results
+
+        anomaly_faces_embeddings_pil = [Image.fromarray(face) for face in anomaly_faces_embeddings]
+        anomaly_frames_posture_pil = [Image.fromarray(frame) for frame in anomaly_frames_posture_images]
+
+        face_samples_frequent = [Image.open(path) for path in face_samples_frequent]
+        face_samples_other = [Image.open(path) for path in face_samples_other]
+
+        anomaly_sentences_features, anomaly_sentences_posture = results[-2:]
+
+        # Format anomaly sentences output
+        sentences_features_output = format_anomaly_sentences(anomaly_sentences_features, "Facial Features")
+        sentences_posture_output = format_anomaly_sentences(anomaly_sentences_posture, "Body Posture")
+
+        output = [
+            exec_time, results_summary,
+            df, mse_embeddings, mse_posture,
+            mse_plot_embeddings, mse_plot_posture,
+            mse_histogram_embeddings, mse_histogram_posture,
+            mse_heatmap_embeddings, mse_heatmap_posture,
+            anomaly_faces_embeddings_pil, anomaly_frames_posture_pil,
+            face_samples_frequent, face_samples_other,
+            aligned_faces_folder, frames_folder,
+            mse_embeddings, mse_posture,
+            sentences_features_output, sentences_posture_output
+        ]
+
+        return output
+
+    except Exception as e:
+        error_message = f"An error occurred: {str(e)}"
+        print(error_message)
+        import traceback
+        traceback.print_exc()
+        return [error_message] + [None] * 20
+
+with gr.Blocks() as iface:
+    gr.Markdown("""
+    # Facial Expression and Body Language Anomaly Detection
+
+    This application analyzes videos to detect anomalies in facial features and body language. 
+    It processes the video frames to extract facial embeddings and body posture, 
+    then uses machine learning techniques to identify unusual patterns or deviations from the norm.
+
+    For more information, visit: [https://github.com/reab5555/Facial-Expression-Anomaly-Detection](https://github.com/reab5555/Facial-Expression-Anomaly-Detection)
+    """)
+
+    with gr.Row():
+        video_input = gr.Video()
+
+    anomaly_threshold = gr.Slider(minimum=1, maximum=5, step=0.1, value=3, label="Anomaly Detection Threshold")
+    fps_slider = gr.Slider(minimum=5, maximum=20, step=1, value=10, label="Frames Per Second")
+    process_btn = gr.Button("Detect Anomalies")
+    progress_bar = gr.Progress()
+    execution_time = gr.Number(label="Execution Time (seconds)")
+
+    with gr.Group(visible=False) as results_group:
+        results_text = gr.TextArea(label="Anomaly Detection Results", lines=4)
+
+        with gr.Tab("Facial Features"):
+            mse_features_plot = gr.Plot(label="MSE: Facial Features")
+            mse_features_hist = gr.Plot(label="MSE Distribution: Facial Features")
+            mse_features_heatmap = gr.Plot(label="MSE Heatmap: Facial Features")
+            anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2, height="auto")
+
+        with gr.Tab("Body Posture"):
+            mse_posture_plot = gr.Plot(label="MSE: Body Posture")
+            mse_posture_hist = gr.Plot(label="MSE Distribution: Body Posture")
+            mse_posture_heatmap = gr.Plot(label="MSE Heatmap: Body Posture")
+            anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2, height="auto")
+
+        with gr.Tab("Sentences"):
+            with gr.Row():
+                anomaly_sentences_features_output = gr.Textbox(label="Sentences before Facial Feature Anomalies",
+                                                               lines=10)
+                anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2,
+                                                     height="auto")
+
+            with gr.Row():
+                anomaly_sentences_posture_output = gr.Textbox(label="Sentences before Body Posture Anomalies", lines=10)
+                anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2,
+                                                    height="auto")
+
+        with gr.Tab("Face Samples"):
+            face_samples_most_frequent = gr.Gallery(label="Most Frequent Person Samples (Target)", columns=6, rows=2, height="auto")
+            face_samples_others = gr.Gallery(label="Other Persons Samples", columns=6, rows=1, height="auto")
+
+    df_store = gr.State()
+    mse_features_store = gr.State()
+    mse_posture_store = gr.State()
+    aligned_faces_folder_store = gr.State()
+    frames_folder_store = gr.State()
+    mse_heatmap_embeddings_store = gr.State()
+    mse_heatmap_posture_store = gr.State()
+
+    def format_anomaly_sentences(anomaly_sentences, anomaly_type):
+        output = f"Sentences before {anomaly_type} Anomalies:\n\n"
+        for anomaly_timecode, sentences in anomaly_sentences:
+            output += f"Anomaly at {anomaly_timecode}:\n"
+            for sentence_timecode, sentence in sentences:
+                output += f"  [{sentence_timecode}] {sentence}\n"
+            output += "\n"
+        return output
+
+    process_btn.click(
+        process_and_show_completion,
+        inputs=[video_input, anomaly_threshold, fps_slider],
+        outputs=[
+            execution_time, results_text, df_store,
+            mse_features_store, mse_posture_store,
+            mse_features_plot, mse_posture_plot,
+            mse_features_hist, mse_posture_hist,
+            mse_features_heatmap, mse_posture_heatmap,
+            anomaly_frames_features, anomaly_frames_posture,
+            face_samples_most_frequent, face_samples_others,
+            aligned_faces_folder_store, frames_folder_store,
+            mse_heatmap_embeddings_store, mse_heatmap_posture_store,
+            anomaly_sentences_features_output, anomaly_sentences_posture_output
+        ]
+    ).then(
+        lambda: gr.Group(visible=True),
+        inputs=None,
+        outputs=[results_group]
+    )
+
+if __name__ == "__main__":
     iface.launch()