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| import gradio as gr | |
| import cv2 | |
| import torch | |
| import numpy as np | |
| from diffusers import StableDiffusionPipeline,AutoPipelineForImage2Image,AutoencoderTiny | |
| from transformers import AutoProcessor, AutoModel, AutoTokenizer | |
| from PIL import Image | |
| # | |
| # | |
| # def predict(prompt, frame): | |
| # generator = torch.manual_seed(params.seed) | |
| # steps = params.steps | |
| # strength = params.strength | |
| # if int(steps * strength) < 1: | |
| # steps = math.ceil(1 / max(0.10, strength)) | |
| # | |
| # prompt = params.prompt | |
| # prompt_embeds = None | |
| # | |
| # results = self.pipe( | |
| # image=frame, | |
| # prompt_embeds=prompt_embeds, | |
| # prompt=prompt, | |
| # negative_prompt=params.negative_prompt, | |
| # generator=generator, | |
| # strength=strength, | |
| # num_inference_steps=steps, | |
| # guidance_scale=1.1, | |
| # width=params.width, | |
| # height=params.height, | |
| # output_type="pil", | |
| # ) | |
| # | |
| # nsfw_content_detected = ( | |
| # results.nsfw_content_detected[0] | |
| # if "nsfw_content_detected" in results | |
| # else False | |
| # ) | |
| # if nsfw_content_detected: | |
| # return None | |
| # result_image = results.images[0] | |
| # | |
| # return result_image | |
| # | |
| # def process_frame(frame, prompt="A futuristic landscape"): | |
| # """Process a single frame using the real-time latent consistency model.""" | |
| # | |
| # # Convert frame to PIL image | |
| # image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).resize((512, 512)) | |
| # | |
| # # Apply Real-Time Latent Consistency Model | |
| # result = realtime_pipe(prompt=prompt, image=image, strength=0.5, guidance_scale=7.5).images[0] | |
| # return np.array(result) | |
| def video_stream(prompt): | |
| """Captures video feed from webcam and sends to the AI model.""" | |
| cap = cv2.VideoCapture(0) | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame = process_frame(frame, prompt) | |
| yield frame # Return processed frame | |
| cap.release() | |
| # Create Gradio App | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## 🎨 Real-Time AI-Enhanced Webcam using Latent Consistency Model (LCM)") | |
| with gr.Row(): | |
| webcam_feed = gr.Camera(streaming=True, label="Live Webcam") | |
| processed_image = gr.Image(label="AI-Enhanced Webcam Feed") | |
| with gr.Row(): | |
| canvas_output = gr.Image(interactive=True, label="Canvas - Processed Image Output") | |
| prompt_input = gr.Textbox(label="Real-Time LCM Prompt", value="A futuristic landscape") | |
| start_button = gr.Button("Start Real-Time AI Enhancement") | |
| #start_button.click(fn=video_stream, inputs=[prompt_input], outputs=[processed_image, canvas_output]) | |
| demo.launch(share=True) | |