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| # import gradio as gr | |
| # import numpy as np | |
| # import random | |
| # # import spaces #[uncomment to use ZeroGPU] | |
| # from diffusers import DiffusionPipeline | |
| # import torch | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use | |
| # if torch.cuda.is_available(): | |
| # torch_dtype = torch.float16 | |
| # else: | |
| # torch_dtype = torch.float32 | |
| # pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype) | |
| # pipe = pipe.to(device) | |
| # MAX_SEED = np.iinfo(np.int32).max | |
| # MAX_IMAGE_SIZE = 1024 | |
| # # @spaces.GPU #[uncomment to use ZeroGPU] | |
| # def infer( | |
| # prompt, | |
| # negative_prompt, | |
| # seed, | |
| # randomize_seed, | |
| # width, | |
| # height, | |
| # guidance_scale, | |
| # num_inference_steps, | |
| # progress=gr.Progress(track_tqdm=True), | |
| # ): | |
| # if randomize_seed: | |
| # seed = random.randint(0, MAX_SEED) | |
| # generator = torch.Generator().manual_seed(seed) | |
| # image = pipe( | |
| # prompt=prompt, | |
| # negative_prompt=negative_prompt, | |
| # guidance_scale=guidance_scale, | |
| # num_inference_steps=num_inference_steps, | |
| # width=width, | |
| # height=height, | |
| # generator=generator, | |
| # ).images[0] | |
| # return image, seed | |
| # examples = [ | |
| # "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| # "An astronaut riding a green horse", | |
| # "A delicious ceviche cheesecake slice", | |
| # ] | |
| # css = """ | |
| # #col-container { | |
| # margin: 0 auto; | |
| # max-width: 640px; | |
| # } | |
| # """ | |
| # with gr.Blocks(css=css) as demo: | |
| # with gr.Column(elem_id="col-container"): | |
| # gr.Markdown(" # Text-to-Image Gradio Template") | |
| # with gr.Row(): | |
| # prompt = gr.Text( | |
| # label="Prompt", | |
| # show_label=False, | |
| # max_lines=1, | |
| # placeholder="Enter your prompt", | |
| # container=False, | |
| # ) | |
| # run_button = gr.Button("Run", scale=0, variant="primary") | |
| # result = gr.Image(label="Result", show_label=False) | |
| # with gr.Accordion("Advanced Settings", open=False): | |
| # negative_prompt = gr.Text( | |
| # label="Negative prompt", | |
| # max_lines=1, | |
| # placeholder="Enter a negative prompt", | |
| # visible=False, | |
| # ) | |
| # seed = gr.Slider( | |
| # label="Seed", | |
| # minimum=0, | |
| # maximum=MAX_SEED, | |
| # step=1, | |
| # value=0, | |
| # ) | |
| # randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| # with gr.Row(): | |
| # width = gr.Slider( | |
| # label="Width", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=1024, # Replace with defaults that work for your model | |
| # ) | |
| # height = gr.Slider( | |
| # label="Height", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=1024, # Replace with defaults that work for your model | |
| # ) | |
| # with gr.Row(): | |
| # guidance_scale = gr.Slider( | |
| # label="Guidance scale", | |
| # minimum=0.0, | |
| # maximum=10.0, | |
| # step=0.1, | |
| # value=0.0, # Replace with defaults that work for your model | |
| # ) | |
| # num_inference_steps = gr.Slider( | |
| # label="Number of inference steps", | |
| # minimum=1, | |
| # maximum=50, | |
| # step=1, | |
| # value=2, # Replace with defaults that work for your model | |
| # ) | |
| # gr.Examples(examples=examples, inputs=[prompt]) | |
| # gr.on( | |
| # triggers=[run_button.click, prompt.submit], | |
| # fn=infer, | |
| # inputs=[ | |
| # prompt, | |
| # negative_prompt, | |
| # seed, | |
| # randomize_seed, | |
| # width, | |
| # height, | |
| # guidance_scale, | |
| # num_inference_steps, | |
| # ], | |
| # outputs=[result, seed], | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch(share=True) | |
| import gradio as gr | |
| import shutil | |
| import os | |
| import subprocess | |
| import sys | |
| # Run the .bat file before launching the app | |
| """try: | |
| import PromptTrack | |
| except ImportError: | |
| print("PromptTrack not found. Installing...") | |
| subprocess.run([sys.executable, "-m", "pip", "install", | |
| "--index-url", "https://test.pypi.org/simple/", | |
| "--extra-index-url", "https://pypi.org/simple/", | |
| "PromptTrack"], check=True) | |
| subprocess.run([sys.executable, "-m", "pip", "install", | |
| "--no-deps", "bytetracker"], check=True) | |
| import PromptTrack # Retry import after installation | |
| from PromptTrack import PromptTracker | |
| tracker = PromptTracker()""" | |
| def process_video(video_path, prompt): | |
| detection_threshold=0.3 | |
| track_thresh=0.4 | |
| match_thresh=1 | |
| max_time_lost=float("inf") | |
| nbr_frames_fixing=800 | |
| output_video = video_path.split('mp4')[0]+"_with_id.mp4" # Placeholder for processed video | |
| output_file = video_path.split('mp4')[0]+"_mot_.json" # Tracking result | |
| output_file_2 = video_path.split('mp4')[0]+"_object_detection.json" # detection results | |
| video_file = video_path | |
| """tracker.detect_objects(video_file, prompt=prompt, nms_threshold=0.8, detection_threshold=detection_threshold, detector="OWL-VITV2") | |
| tracker.process_mot(video_file, fixed_parc=True, track_thresh=track_thresh, match_thresh=match_thresh, frame_rate=25, max_time_lost=max_time_lost, nbr_frames_fixing=nbr_frames_fixing) | |
| tracker.read_video_with_mot(video_file, fps=25)""" | |
| output_video = "output.mp4" # Placeholder for processed video | |
| output_file = "output.txt" # Placeholder for generated file | |
| # Copy the input video to simulate processing | |
| shutil.copy(video_path.name, output_video) | |
| # Create an output text file with the prompt content | |
| with open(output_file, "w") as f: | |
| f.write(f"User Prompt: {prompt}\n") | |
| return output_video, output_file | |
| # Define Gradio interface | |
| iface = gr.Interface( | |
| fn=process_video, | |
| inputs=[gr.File(label="Upload Video"), gr.Textbox(placeholder="Enter your prompt")], | |
| outputs=[gr.Video(), gr.File(label="Generated File")], | |
| title="Video Processing App", | |
| description="Upload a video and enter a prompt. The app will return the processed video and a generated file." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch(share=True) | |
| ''' | |
| import gradio as gr | |
| import shutil | |
| import os | |
| def process_video(video, prompt): | |
| output_video = "output.mp4" # Placeholder for processed video | |
| output_file = "output.txt" # Placeholder for generated file | |
| # Copy the input video to simulate processing | |
| shutil.copy(video.name, output_video) | |
| # Create an output text file with the prompt content | |
| with open(output_file, "w") as f: | |
| f.write(f"User Prompt: {prompt}\n") | |
| return output_video, output_file | |
| # Define Gradio interface | |
| iface = gr.Interface( | |
| fn=process_video, | |
| inputs=[gr.File(label="Upload Video"), gr.Textbox(placeholder="Enter your prompt")], | |
| outputs=[gr.Video(), gr.File(label="Generated File")], | |
| title="Video Processing App", | |
| description="Upload a video and enter a prompt. The app will return the processed video and a generated file." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| iface.launch(share=True)''' |