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| import gradio as gr | |
| import torch | |
| from transformers import AutoProcessor, AutoModel | |
| from PIL import Image, ImageDraw, ImageFont | |
| import numpy as np | |
| import random | |
| import os | |
| import wget # To download weights | |
| # --- Configuration & Model Loading --- | |
| # Device Selection | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| print(f"Using device: {DEVICE}") | |
| # --- CLIP Setup --- | |
| CLIP_MODEL_ID = "openai/clip-vit-base-patch32" | |
| clip_processor = None | |
| clip_model = None | |
| def load_clip_model(): | |
| global clip_processor, clip_model | |
| if clip_processor is None: | |
| print(f"Loading CLIP processor: {CLIP_MODEL_ID}...") | |
| clip_processor = AutoProcessor.from_pretrained(CLIP_MODEL_ID) | |
| print("CLIP processor loaded.") | |
| if clip_model is None: | |
| print(f"Loading CLIP model: {CLIP_MODEL_ID}...") | |
| clip_model = AutoModel.from_pretrained(CLIP_MODEL_ID).to(DEVICE) | |
| print(f"CLIP model loaded to {DEVICE}.") | |
| # --- FastSAM Setup --- | |
| # Use a smaller model suitable for Spaces CPU/basic GPU if needed | |
| FASTSAM_CHECKPOINT = "FastSAM-s.pt" | |
| FASTSAM_CHECKPOINT_URL = f"https://huggingface.co/spaces/An-619/FastSAM/resolve/main/{FASTSAM_CHECKPOINT}" # Example URL, find official if possible | |
| fastsam_model = None | |
| def download_fastsam_weights(): | |
| if not os.path.exists(FASTSAM_CHECKPOINT): | |
| print(f"Downloading FastSAM weights: {FASTSAM_CHECKPOINT}...") | |
| try: | |
| wget.download(FASTSAM_CHECKPOINT_URL, FASTSAM_CHECKPOINT) | |
| print("FastSAM weights downloaded.") | |
| except Exception as e: | |
| print(f"Error downloading FastSAM weights: {e}") | |
| print("Please ensure the URL is correct and reachable, or manually place the weights file.") | |
| return False | |
| return os.path.exists(FASTSAM_CHECKPOINT) | |
| def load_fastsam_model(): | |
| global fastsam_model | |
| if fastsam_model is None: | |
| if download_fastsam_weights(): | |
| try: | |
| from fastsam import FastSAM, FastSAMPrompt # Import here after potential download | |
| print(f"Loading FastSAM model: {FASTSAM_CHECKPOINT}...") | |
| fastsam_model = FastSAM(FASTSAM_CHECKPOINT) | |
| print(f"FastSAM model loaded.") # Device handled internally by FastSAM based on its setup/torch device | |
| except ImportError: | |
| print("Error: 'fastsam' library not found. Please install it (pip install fastsam).") | |
| except Exception as e: | |
| print(f"Error loading FastSAM model: {e}") | |
| else: | |
| print("FastSAM weights not found. Cannot load model.") | |
| # --- Processing Functions --- | |
| # CLIP Zero-Shot Classification Function | |
| def run_clip_zero_shot(image: Image.Image, text_labels: str): | |
| if clip_model is None or clip_processor is None: | |
| load_clip_model() # Attempt to load if not already loaded | |
| if clip_model is None: | |
| return "Error: CLIP Model not loaded. Check logs.", None | |
| if not text_labels: | |
| return "Please provide comma-separated text labels.", None | |
| if image is None: | |
| return "Please upload an image.", None | |
| labels = [label.strip() for label in text_labels.split(',')] | |
| if not labels: | |
| return "No valid labels provided.", None | |
| print(f"Running CLIP zero-shot classification with labels: {labels}") | |
| try: | |
| # Ensure image is RGB | |
| if image.mode != "RGB": | |
| image = image.convert("RGB") | |
| inputs = clip_processor(text=labels, images=image, return_tensors="pt", padding=True).to(DEVICE) | |
| with torch.no_grad(): | |
| outputs = clip_model(**inputs) | |
| logits_per_image = outputs.logits_per_image # this is the image-text similarity score | |
| probs = logits_per_image.softmax(dim=1) # convert to probabilities | |
| print("CLIP processing complete.") | |
| # Format output for Gradio Label | |
| confidences = {labels[i]: float(probs[0, i].item()) for i in range(len(labels))} | |
| return confidences, image # Return original image for display alongside results | |
| except Exception as e: | |
| print(f"Error during CLIP processing: {e}") | |
| return f"An error occurred: {e}", None | |
| # FastSAM Segmentation Function | |
| def run_fastsam_segmentation(image_pil: Image.Image, conf_threshold: float = 0.4, iou_threshold: float = 0.9): | |
| if fastsam_model is None: | |
| load_fastsam_model() # Attempt to load if not already loaded | |
| if fastsam_model is None: | |
| return "Error: FastSAM Model not loaded. Check logs.", None | |
| if image_pil is None: | |
| return "Please upload an image.", None | |
| print("Running FastSAM segmentation...") | |
| try: | |
| # Ensure image is RGB | |
| if image_pil.mode != "RGB": | |
| image_pil = image_pil.convert("RGB") | |
| # FastSAM expects a BGR numpy array or path usually. Let's try with RGB numpy. | |
| # If it fails, uncomment the BGR conversion line. | |
| image_np_rgb = np.array(image_pil) | |
| # image_np_bgr = image_np_rgb[:, :, ::-1] # Convert RGB to BGR if needed | |
| # Run FastSAM inference | |
| # Adjust imgsz, conf, iou as needed. Higher imgsz = more detail, slower. | |
| everything_results = fastsam_model( | |
| image_np_rgb, # Use image_np_bgr if conversion needed | |
| device=DEVICE, | |
| retina_masks=True, | |
| imgsz=640, # Smaller size for faster inference on limited hardware | |
| conf=conf_threshold, | |
| iou=iou_threshold, | |
| ) | |
| # Process results using FastSAMPrompt | |
| from fastsam import FastSAMPrompt # Make sure it's imported | |
| prompt_process = FastSAMPrompt(image_np_rgb, everything_results, device=DEVICE) | |
| # Get all annotations (masks) | |
| ann = prompt_process.everything_prompt() | |
| print(f"FastSAM found {len(ann[0]['masks']) if ann and ann[0] else 0} masks.") | |
| # --- Plotting Masks on Image (Manual) --- | |
| output_image = image_pil.copy() | |
| if ann and ann[0] is not None and 'masks' in ann[0] and len(ann[0]['masks']) > 0: | |
| masks = ann[0]['masks'].cpu().numpy() # shape (N, H, W) | |
| # Create overlay image | |
| overlay = Image.new('RGBA', output_image.size, (0, 0, 0, 0)) | |
| draw = ImageDraw.Draw(overlay) | |
| for i in range(masks.shape[0]): | |
| mask = masks[i] # shape (H, W), boolean | |
| # Generate random color with some transparency | |
| color = (random.randint(50, 255), random.randint(50, 255), random.randint(50, 255), 128) # RGBA with alpha | |
| # Create a single-channel image from the boolean mask | |
| mask_image = Image.fromarray((mask * 255).astype(np.uint8), mode='L') | |
| # Apply color to the mask area on the overlay | |
| draw.bitmap((0,0), mask_image, fill=color) | |
| # Composite the overlay onto the original image | |
| output_image = Image.alpha_composite(output_image.convert('RGBA'), overlay).convert('RGB') | |
| print("FastSAM processing and plotting complete.") | |
| return output_image, image_pil # Return segmented and original images | |
| except Exception as e: | |
| print(f"Error during FastSAM processing: {e}") | |
| import traceback | |
| traceback.print_exc() # Print detailed traceback | |
| return f"An error occurred: {e}", None | |
| # --- Gradio Interface --- | |
| # Pre-load models on startup (optional but good for performance) | |
| print("Attempting to preload models...") | |
| load_clip_model() | |
| load_fastsam_model() | |
| print("Preloading finished (or attempted).") | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# CLIP & FastSAM Demo") | |
| gr.Markdown("Explore Zero-Shot Classification with CLIP and 'Segment Anything' with FastSAM.") | |
| with gr.Tabs(): | |
| # --- CLIP Tab --- | |
| with gr.TabItem("CLIP Zero-Shot Classification"): | |
| gr.Markdown("Upload an image and provide comma-separated candidate labels (e.g., 'cat, dog, car'). CLIP will predict the probability of the image matching each label.") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| clip_input_image = gr.Image(type="pil", label="Input Image") | |
| clip_text_labels = gr.Textbox(label="Comma-Separated Labels", placeholder="e.g., astronaut, mountain, dog playing fetch") | |
| clip_button = gr.Button("Run CLIP Classification", variant="primary") | |
| with gr.Column(scale=1): | |
| clip_output_label = gr.Label(label="Classification Probabilities") | |
| clip_output_image_display = gr.Image(type="pil", label="Input Image Preview") # Show input for context | |
| clip_button.click( | |
| run_clip_zero_shot, | |
| inputs=[clip_input_image, clip_text_labels], | |
| outputs=[clip_output_label, clip_output_image_display] | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["examples/astronaut.jpg", "astronaut, moon, rover, mountain"], | |
| ["examples/dog_bike.jpg", "dog, bicycle, person, park, grass"], | |
| ], | |
| inputs=[clip_input_image, clip_text_labels], | |
| outputs=[clip_output_label, clip_output_image_display], | |
| fn=run_clip_zero_shot, | |
| cache_examples=False, # Re-run for live demo | |
| ) | |
| # --- FastSAM Tab --- | |
| with gr.TabItem("FastSAM Segmentation"): | |
| gr.Markdown("Upload an image. FastSAM will attempt to segment all objects/regions in the image.") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| fastsam_input_image = gr.Image(type="pil", label="Input Image") | |
| with gr.Row(): | |
| fastsam_conf = gr.Slider(minimum=0.1, maximum=1.0, value=0.4, step=0.05, label="Confidence Threshold") | |
| fastsam_iou = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="IoU Threshold") | |
| fastsam_button = gr.Button("Run FastSAM Segmentation", variant="primary") | |
| with gr.Column(scale=1): | |
| fastsam_output_image = gr.Image(type="pil", label="Segmented Image") | |
| # fastsam_input_display = gr.Image(type="pil", label="Original Image") # Optional: show original side-by-side | |
| fastsam_button.click( | |
| run_fastsam_segmentation, | |
| inputs=[fastsam_input_image, fastsam_conf, fastsam_iou], | |
| outputs=[fastsam_output_image] # Removed the second output for simplicity, adjust if needed | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| ["examples/dogs.jpg", 0.4, 0.9], | |
| ["examples/fruits.jpg", 0.5, 0.8], | |
| ], | |
| inputs=[fastsam_input_image, fastsam_conf, fastsam_iou], | |
| outputs=[fastsam_output_image], | |
| fn=run_fastsam_segmentation, | |
| cache_examples=False, # Re-run for live demo | |
| ) | |
| # Add example images (optional, but helpful) | |
| # Create an 'examples' folder and add some jpg images like 'astronaut.jpg', 'dog_bike.jpg', 'dogs.jpg', 'fruits.jpg' | |
| if not os.path.exists("examples"): | |
| os.makedirs("examples") | |
| print("Created 'examples' directory. Please add some images (e.g., astronaut.jpg, dog_bike.jpg) for the examples to work.") | |
| # You might need to download some sample images here too if running on a fresh env | |
| try: | |
| print("Downloading example images...") | |
| wget.download("https://huggingface.co/spaces/gradio/image-segmentation/resolve/main/images/lion.jpg", "examples/lion.jpg") | |
| wget.download("https://raw.githubusercontent.com/openai/CLIP/main/CLIP.png", "examples/clip_logo.png") | |
| wget.download("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-logo.png", "examples/gradio_logo.png") | |
| # Manually add the examples used above if these don't match | |
| print("Example images downloaded (or attempted). Please verify.") | |
| except Exception as e: | |
| print(f"Could not download example images: {e}") | |
| # Launch the Gradio app | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) # Set debug=False for deployment |