Update app.py

app.py

@@ -1,198 +1,197 @@
-import time
-import uuid
-
-import cv2
-import gradio as gr
-import numpy as np
-import spaces  
-import supervision as sv
-import torch  # Ensuring torch import remains
-
-from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor
-
-# Detect if CUDA is available and set the device accordingly
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Load the processor and model from Hugging Face
-processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
-model = AutoModelForZeroShotObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf").to(device)
-
-# Custom CSS to enhance text area visibility
-css = """
-.feedback textarea {font-size: 24px !important}
-"""
-
-# Initialize global variables
-global classes
-global detections
-global labels
-global threshold
-
-# Set default values
-classes = "person, university, class, Liectenstein"
-detections = None
-labels = None
-threshold = 0.2
-
-# Instantiate annotators for bounding boxes, masks, and labels
-BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
-MASK_ANNOTATOR = sv.MaskAnnotator()
-LABEL_ANNOTATOR = sv.LabelAnnotator()
-
-# Frame subsampling factor for video processing efficiency
-SUBSAMPLE = 2
-
-
-def annotate_image(input_image, detections, labels) -> np.ndarray:
-    """Applies mask, bounding box, and label annotations to a given image."""
-    output_image = MASK_ANNOTATOR.annotate(input_image, detections)
-    output_image = BOUNDING_BOX_ANNOTATOR.annotate(output_image, detections)
-    output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
-    return output_image
-
-
-@spaces.GPU
-def process_video(input_video, confidence_threshold, classes_new, progress=gr.Progress(track_tqdm=True)):
-    """Processes the input video frame by frame, performs object detection, and saves the output video."""
-    global detections, labels, classes, threshold
-    classes = classes_new
-    threshold = confidence_threshold
-
-    # Generate a unique file name for the output video
-    result_file_name = f"output_{uuid.uuid4()}.mp4"
-
-    # Read input video and set up output video writer
-    cap = cv2.VideoCapture(input_video)
-    video_codec = cv2.VideoWriter_fourcc(*"mp4v")  # MP4 codec
-    fps = int(cap.get(cv2.CAP_PROP_FPS))
-    width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    desired_fps = fps // SUBSAMPLE
-    iterating, frame = cap.read()
-
-    # Prepare video writer for output
-    segment_file = cv2.VideoWriter(result_file_name, video_codec, desired_fps, (width, height))
-    batch, frames, predict_index = [], [], []
-    n_frames = 0
-
-    while iterating:
-        if n_frames % SUBSAMPLE == 0:
-            predict_index.append(len(frames))
-            batch.append(frame)
-            frames.append(frame)
-
-        # Process a batch of frames at once
-        if len(batch) == desired_fps:
-            classes_list = classes.strip().split(",")
-            results, fps_value = query(batch, classes_list, threshold, (width, height))
-
-            for i, frame in enumerate(frames):
-                if i in predict_index:
-                    batch_idx = predict_index.index(i)
-                    detections = sv.Detections(
-                        xyxy=results[batch_idx]["boxes"].cpu().detach().numpy(),
-                        confidence=results[batch_idx]["scores"].cpu().detach().numpy(),
-                        class_id=np.array([classes_list.index(result_class) for result_class in results[batch_idx]["classes"]]),
-                        data={"class_name": results[batch_idx]["classes"]},
-                    )
-                    labels = results[batch_idx]["classes"]
-
-                frame = annotate_image(input_image=frame, detections=detections, labels=labels)
-                segment_file.write(frame)
-
-            # Finalize and yield result
-            segment_file.release()
-            yield result_file_name, gr.Markdown(f'<h3 style="text-align: center;">Model inference FPS (batched): {fps_value * len(batch):.2f}</h3>')
-            result_file_name = f"output_{uuid.uuid4()}.mp4"
-            segment_file = cv2.VideoWriter(result_file_name, video_codec, desired_fps, (width, height))
-            batch.clear()
-            frames.clear()
-            predict_index.clear()
-
-        iterating, frame = cap.read()
-        n_frames += 1
-
-
-def query(frame_batch, classes, confidence_threshold, size=(640, 480)):
-    """Runs inference on a batch of frames and returns the results."""
-    inputs = processor(images=frame_batch, text=[classes] * len(frame_batch), return_tensors="pt").to(device)
-
-    with torch.no_grad():
-        start_time = time.time()
-        outputs = model(**inputs)
-        fps_value = 1 / (time.time() - start_time)
-
-    target_sizes = torch.tensor([size[::-1]] * len(frame_batch))
-    results = processor.post_process_grounded_object_detection(
-        outputs=outputs, classes=[classes] * len(frame_batch), score_threshold=confidence_threshold, target_sizes=target_sizes
-    )
-
-    return results, fps_value
-
-
-def set_classes(classes_input):
-    """Updates the list of classes for detection."""
-    global classes
-    classes = classes_input
-
-
-def set_confidence_threshold(confidence_threshold_input):
-    """Updates the confidence threshold for detection."""
-    global threshold
-    threshold = confidence_threshold_input
-
-
-# Custom footer for the Gradio interface
-footer = """
-<div style="text-align: center; margin-top: 20px;">
-    <a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
-    <a href="https://github.com/arad1367" target="_blank">GitHub</a> |
-    <a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> |
-    <a href="https://huggingface.co/omlab/omdet-turbo-swin-tiny-hf" target="_blank">omdet-turbo-swin-tiny-hf repo in HF</a>  
-    <br>
-    Made with 💖 by Pejman Ebrahimi
-</div>
-"""
-
-# Gradio Interface with the customized theme and DuplicateButton
-with gr.Blocks(theme='ParityError/Anime', css=css) as demo:
-    gr.Markdown("## Real Time Object Detection with OmDet-Turbo")
-    gr.Markdown(
-        """
-        This is a demo for real-time open vocabulary object detection using OmDet-Turbo.<br>
-        It utilizes ZeroGPU, which allocates GPU for the first inference.<br>
-        The actual inference FPS is displayed after processing, providing an accurate assessment of performance.<br>
-        """
-    )
-    
-    with gr.Row():
-        input_video = gr.Video(label="Upload Video")
-        output_video = gr.Video(label="Processed Video", streaming=True, autoplay=True)
-        actual_fps = gr.Markdown("", visible=False)
-    
-    with gr.Row():
-        classes = gr.Textbox("person, university, class, Liectenstein", label="Objects to Detect (comma separated)", elem_classes="feedback", scale=3)
-        conf = gr.Slider(label="Confidence Threshold", minimum=0.1, maximum=1.0, value=0.2, step=0.05)
-
-    with gr.Row():
-        submit = gr.Button("Run Detection", variant="primary")
-        duplicate_space = gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
-
-    example_videos = gr.Examples(
-        examples=[["./UNI-LI.mp4", 0.3, "person, university, class, Liectenstein"]],
-        inputs=[input_video, conf, classes],
-        outputs=[output_video, actual_fps]
-    )
-
-    classes.submit(set_classes, classes)
-    conf.change(set_confidence_threshold, conf)
-
-    submit.click(
-        fn=process_video,
-        inputs=[input_video, conf, classes],
-        outputs=[output_video, actual_fps]
-    )
-
-    gr.HTML(footer)
-
-if __name__ == "__main__":
-    demo.launch(show_error=True)
+import time
+import uuid
+
+import cv2
+import gradio as gr
+import numpy as np
+import spaces
+import supervision as sv
+import torch
+
+from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor
+
+# Detect if CUDA is available and set the device accordingly
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the processor and model from Hugging Face
+processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf")
+model = AutoModelForZeroShotObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf").to(device)
+
+# Custom CSS to enhance text area visibility
+css = """
+.feedback textarea {font-size: 24px !important}
+"""
+
+# Initialize global variables
+global classes
+global detections
+global labels
+global threshold
+
+# Set default values
+classes = "person, university, class, Liectenstein"
+detections = None
+labels = None
+threshold = 0.2
+
+# Instantiate annotators for bounding boxes, masks, and labels
+BOX_ANNOTATOR = sv.BoxAnnotator()  # Updated from BoundingBoxAnnotator
+MASK_ANNOTATOR = sv.MaskAnnotator()
+LABEL_ANNOTATOR = sv.LabelAnnotator()
+
+# Frame subsampling factor for video processing efficiency
+SUBSAMPLE = 2
+
+def annotate_image(input_image, detections, labels) -> np.ndarray:
+    """Applies mask, bounding box, and label annotations to a given image."""
+    output_image = MASK_ANNOTATOR.annotate(input_image, detections)
+    output_image = BOX_ANNOTATOR.annotate(output_image, detections)  # Updated
+    output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
+    return output_image
+
+
+@spaces.GPU
+def process_video(input_video, confidence_threshold, classes_new, progress=gr.Progress(track_tqdm=True)):
+    """Processes the input video frame by frame, performs object detection, and saves the output video."""
+    global detections, labels, classes, threshold
+    classes = classes_new
+    threshold = confidence_threshold
+
+    # Generate a unique file name for the output video
+    result_file_name = f"output_{uuid.uuid4()}.mp4"
+
+    # Read input video and set up output video writer
+    cap = cv2.VideoCapture(input_video)
+    video_codec = cv2.VideoWriter_fourcc(*"mp4v")  # MP4 codec
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    desired_fps = fps // SUBSAMPLE
+    iterating, frame = cap.read()
+
+    # Prepare video writer for output
+    segment_file = cv2.VideoWriter(result_file_name, video_codec, desired_fps, (width, height))
+    batch, frames, predict_index = [], [], []
+    n_frames = 0
+
+    while iterating:
+        if n_frames % SUBSAMPLE == 0:
+            predict_index.append(len(frames))
+            batch.append(frame)
+            frames.append(frame)
+
+        # Process a batch of frames at once
+        if len(batch) == desired_fps:
+            classes_list = classes.strip().split(",")
+            results, fps_value = query(batch, classes_list, threshold, (width, height))
+
+            for i, frame in enumerate(frames):
+                if i in predict_index:
+                    batch_idx = predict_index.index(i)
+                    detections = sv.Detections(
+                        xyxy=results[batch_idx]["boxes"].cpu().detach().numpy(),
+                        confidence=results[batch_idx]["scores"].cpu().detach().numpy(),
+                        class_id=np.array([classes_list.index(result_class) for result_class in results[batch_idx]["classes"]]),
+                        data={"class_name": results[batch_idx]["classes"]},
+                    )
+                    labels = results[batch_idx]["classes"]
+
+                frame = annotate_image(input_image=frame, detections=detections, labels=labels)
+                segment_file.write(frame)
+
+            # Finalize and yield result
+            segment_file.release()
+            yield result_file_name, gr.Markdown(f'<h3 style="text-align: center;">Model inference FPS (batched): {fps_value * len(batch):.2f}</h3>')
+            result_file_name = f"output_{uuid.uuid4()}.mp4"
+            segment_file = cv2.VideoWriter(result_file_name, video_codec, desired_fps, (width, height))
+            batch.clear()
+            frames.clear()
+            predict_index.clear()
+
+        iterating, frame = cap.read()
+        n_frames += 1
+
+
+def query(frame_batch, classes, confidence_threshold, size=(640, 480)):
+    """Runs inference on a batch of frames and returns the results."""
+    inputs = processor(images=frame_batch, text=[classes] * len(frame_batch), return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        start_time = time.time()
+        outputs = model(**inputs)
+        fps_value = 1 / (time.time() - start_time)
+
+    target_sizes = torch.tensor([size[::-1]] * len(frame_batch))
+    results = processor.post_process_grounded_object_detection(
+        outputs=outputs, classes=[classes] * len(frame_batch), score_threshold=confidence_threshold, target_sizes=target_sizes
+    )
+
+    return results, fps_value
+
+
+def set_classes(classes_input):
+    """Updates the list of classes for detection."""
+    global classes
+    classes = classes_input
+
+
+def set_confidence_threshold(confidence_threshold_input):
+    """Updates the confidence threshold for detection."""
+    global threshold
+    threshold = confidence_threshold_input
+
+
+# Custom footer for the Gradio interface
+footer = """
+<div style="text-align: center; margin-top: 20px;">
+    <a href="https://www.linkedin.com/in/pejman-ebrahimi-4a60151a7/" target="_blank">LinkedIn</a> |
+    <a href="https://github.com/arad1367" target="_blank">GitHub</a> |
+    <a href="https://arad1367.pythonanywhere.com/" target="_blank">Live demo of my PhD defense</a> |
+    <a href="https://huggingface.co/omlab/omdet-turbo-swin-tiny-hf" target="_blank">omdet-turbo-swin-tiny-hf repo in HF</a>  
+    <br>
+    Made with 💖 by Pejman Ebrahimi
+</div>
+"""
+
+# Gradio Interface with the customized theme and DuplicateButton
+with gr.Blocks(theme='ParityError/Anime', css=css) as demo:
+    gr.Markdown("## Real Time Object Detection with OmDet-Turbo")
+    gr.Markdown(
+        """
+        This is a demo for real-time open vocabulary object detection using OmDet-Turbo.<br>
+        It utilizes ZeroGPU, which allocates GPU for the first inference.<br>
+        The actual inference FPS is displayed after processing, providing an accurate assessment of performance.<br>
+        """
+    )
+    
+    with gr.Row():
+        input_video = gr.Video(label="Upload Video")
+        output_video = gr.Video(label="Processed Video", autoplay=True)  # Removed 'streaming' argument
+        actual_fps = gr.Markdown("", visible=False)
+    
+    with gr.Row():
+        classes = gr.Textbox("person, university, class, Liectenstein", label="Objects to Detect (comma separated)", elem_classes="feedback", scale=3)
+        conf = gr.Slider(label="Confidence Threshold", minimum=0.1, maximum=1.0, value=0.2, step=0.05)
+
+    with gr.Row():
+        submit = gr.Button("Run Detection", variant="primary")
+        duplicate_space = gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+
+    example_videos = gr.Examples(
+        examples=[["./UNI-LI.mp4", 0.3, "person, university, class, Liectenstein"]],
+        inputs=[input_video, conf, classes],
+        outputs=[output_video, actual_fps]
+    )
+
+    classes.submit(set_classes, classes)
+    conf.change(set_confidence_threshold, conf)
+
+    submit.click(
+        fn=process_video,
+        inputs=[input_video, conf, classes],
+        outputs=[output_video, actual_fps]
+    )
+
+    gr.HTML(footer)
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)