import torch from PIL import Image import requests from openai import OpenAI from transformers import (Owlv2Processor, Owlv2ForObjectDetection, AutoProcessor, AutoModelForMaskGeneration) import matplotlib.pyplot as plt import matplotlib.patches as patches import base64 import io import numpy as np import gradio as gr import json import os from dotenv import load_dotenv # Load environment variables load_dotenv() OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') def encode_image_to_base64(image): # If image is a tuple (e.g., Gradio input), take the first element if isinstance(image, tuple): image = image[0] # Extract the image from the tuple # If image is a numpy array, convert it to a PIL Image if isinstance(image, np.ndarray): image = Image.fromarray(image) # Ensure image is in PIL Image format if not isinstance(image, Image.Image): raise ValueError("Input must be a PIL Image, numpy array, or tuple containing an image") buffered = io.BytesIO() image.save(buffered, format="PNG") return base64.b64encode(buffered.getvalue()).decode('utf-8') def analyze_image(image): client = OpenAI(api_key=OPENAI_API_KEY) base64_image = encode_image_to_base64(image) messages = [ { "role": "user", "content": [ { "type": "text", "text": """Your task is to determine if the image is surprising or not surprising. if the image is surprising, determine which element, figure or object in the image is making the image surprising and write it only in one sentence with no more then 6 words, otherwise, write 'NA'. Also rate how surprising the image is on a scale of 1-5, where 1 is not surprising at all and 5 is highly surprising. Provide the response as a JSON with the following structure: { "label": "[surprising OR not surprising]", "element": "[element]", "rating": [1-5] }""" }, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64_image}" } } ] } ] response = client.chat.completions.create( model="gpt-4o-mini", messages=messages, max_tokens=100, temperature=0.1, response_format={ "type": "json_object" } ) return response.choices[0].message.content def show_mask(mask, ax, random_color=False): if random_color: color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0) else: color = np.array([1.0, 0.0, 0.0, 0.5]) if len(mask.shape) == 4: mask = mask[0, 0] mask_image = np.zeros((*mask.shape, 4), dtype=np.float32) mask_image[mask > 0] = color ax.imshow(mask_image) def process_image_detection(image, target_label, surprise_rating): device = "cuda" if torch.cuda.is_available() else "cpu" # Get original image DPI and size original_dpi = image.info.get('dpi', (72, 72)) original_size = image.size # Calculate relative font size based on image dimensions base_fontsize = min(original_size) / 40 # Adjust this divisor to change overall font size owlv2_processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16") owlv2_model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16").to(device) sam_processor = AutoProcessor.from_pretrained("facebook/sam-vit-base") sam_model = AutoModelForMaskGeneration.from_pretrained("facebook/sam-vit-base").to(device) image_np = np.array(image) inputs = owlv2_processor(text=[target_label], images=image, return_tensors="pt").to(device) with torch.no_grad(): outputs = owlv2_model(**inputs) target_sizes = torch.tensor([image.size[::-1]]).to(device) results = owlv2_processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0] dpi = 300 # Increased DPI for better text rendering figsize = (original_size[0] / dpi, original_size[1] / dpi) fig = plt.figure(figsize=figsize, dpi=dpi) ax = plt.Axes(fig, [0., 0., 1., 1.]) fig.add_axes(ax) plt.imshow(image) scores = results["scores"] if len(scores) > 0: max_score_idx = scores.argmax().item() max_score = scores[max_score_idx].item() if max_score > 0.2: box = results["boxes"][max_score_idx].cpu().numpy() sam_inputs = sam_processor( image, input_boxes=[[[box[0], box[1], box[2], box[3]]]], return_tensors="pt" ).to(device) with torch.no_grad(): sam_outputs = sam_model(**sam_inputs) masks = sam_processor.image_processor.post_process_masks( sam_outputs.pred_masks.cpu(), sam_inputs["original_sizes"].cpu(), sam_inputs["reshaped_input_sizes"].cpu() ) mask = masks[0].numpy() if isinstance(masks[0], torch.Tensor) else masks[0] show_mask(mask, ax=ax) # Draw rectangle with increased line width rect = patches.Rectangle( (box[0], box[1]), box[2] - box[0], box[3] - box[1], linewidth=max(2, min(original_size) / 500), # Scale line width with image size edgecolor='red', facecolor='none' ) ax.add_patch(rect) # Add confidence score with improved visibility plt.text( box[0], box[1] - base_fontsize, f'{max_score:.2f}', color='red', fontsize=base_fontsize, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='none', pad=2) ) # Add label and rating with improved visibility plt.text( box[2] + base_fontsize / 2, box[1], f'Unexpected (Rating: {surprise_rating}/5)\n{target_label}', color='red', fontsize=base_fontsize, fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, edgecolor='none', pad=2), verticalalignment='bottom' ) plt.axis('off') # Save with high DPI buf = io.BytesIO() plt.savefig(buf, format='png', dpi=dpi, bbox_inches='tight', pad_inches=0, metadata={'dpi': original_dpi}) buf.seek(0) plt.close() # Process final image output_image = Image.open(buf) output_image = output_image.resize(original_size, Image.Resampling.LANCZOS) final_buf = io.BytesIO() output_image.save(final_buf, format='PNG', dpi=original_dpi) final_buf.seek(0) return final_buf def process_and_analyze(image): if image is None: return None, "Please upload an image first." if OPENAI_API_KEY is None: return None, "OpenAI API key not found in environment variables." try: # Handle different input types if isinstance(image, tuple): image = image[0] # Take the first element if it's a tuple if isinstance(image, np.ndarray): image = Image.fromarray(image) if not isinstance(image, Image.Image): raise ValueError("Invalid image format") # Analyze image gpt_response = analyze_image(image) response_data = json.loads(gpt_response) if response_data["label"].lower() == "surprising" and response_data["element"].lower() != "na": result_buf = process_image_detection(image, response_data["element"], response_data["rating"]) result_image = Image.open(result_buf) analysis_text = f"Label: {response_data['label']}\nElement: {response_data['element']}\nRating: {response_data['rating']}/5" return result_image, analysis_text else: return image, "Not Surprising" except Exception as e: return None, f"Error processing image: {str(e)}" # Create Gradio interface def create_interface(): with gr.Blocks() as demo: gr.Markdown("# Image Surprise Analysis") with gr.Row(): with gr.Column(): input_image = gr.Image(label="Upload Image") analyze_btn = gr.Button("Analyze Image") with gr.Column(): output_image = gr.Image(label="Processed Image") output_text = gr.Textbox(label="Analysis Results") analyze_btn.click( fn=process_and_analyze, inputs=[input_image], outputs=[output_image, output_text] ) return demo if __name__ == "__main__": demo = create_interface() demo.launch()