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| # import os | |
| # import numpy as np | |
| # import torch | |
| # import torch.nn as nn | |
| # import gradio as gr | |
| # import time | |
| # from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights | |
| # from torchvision.ops import nms, box_iou | |
| # import torch.nn.functional as F | |
| # from torchvision import transforms | |
| # from PIL import Image, ImageDraw, ImageFont, ImageFilter | |
| # from breed_health_info import breed_health_info | |
| # from breed_noise_info import breed_noise_info | |
| # from dog_database import get_dog_description, dog_data | |
| # from scoring_calculation_system import UserPreferences | |
| # from recommendation_html_format import format_recommendation_html, get_breed_recommendations | |
| # from history_manager import UserHistoryManager | |
| # from search_history import create_history_tab, create_history_component | |
| # from styles import get_css_styles | |
| # from breed_detection import create_detection_tab | |
| # from breed_comparison import create_comparison_tab | |
| # from breed_recommendation import create_recommendation_tab | |
| # from html_templates import ( | |
| # format_description_html, | |
| # format_single_dog_result, | |
| # format_multiple_breeds_result, | |
| # format_error_message, | |
| # format_warning_html, | |
| # format_multi_dog_container, | |
| # format_breed_details_html, | |
| # get_color_scheme, | |
| # get_akc_breeds_link | |
| # ) | |
| # from urllib.parse import quote | |
| # from ultralytics import YOLO | |
| # import asyncio | |
| # import traceback | |
| # model_yolo = YOLO('yolov8l.pt') | |
| # history_manager = UserHistoryManager() | |
| # dog_breeds = ["Afghan_Hound", "African_Hunting_Dog", "Airedale", "American_Staffordshire_Terrier", | |
| # "Appenzeller", "Australian_Terrier", "Bedlington_Terrier", "Bernese_Mountain_Dog", "Bichon_Frise", | |
| # "Blenheim_Spaniel", "Border_Collie", "Border_Terrier", "Boston_Bull", "Bouvier_Des_Flandres", | |
| # "Brabancon_Griffon", "Brittany_Spaniel", "Cardigan", "Chesapeake_Bay_Retriever", | |
| # "Chihuahua", "Dachshund", "Dandie_Dinmont", "Doberman", "English_Foxhound", "English_Setter", | |
| # "English_Springer", "EntleBucher", "Eskimo_Dog", "French_Bulldog", "German_Shepherd", | |
| # "German_Short-Haired_Pointer", "Gordon_Setter", "Great_Dane", "Great_Pyrenees", | |
| # "Greater_Swiss_Mountain_Dog","Havanese", "Ibizan_Hound", "Irish_Setter", "Irish_Terrier", | |
| # "Irish_Water_Spaniel", "Irish_Wolfhound", "Italian_Greyhound", "Japanese_Spaniel", | |
| # "Kerry_Blue_Terrier", "Labrador_Retriever", "Lakeland_Terrier", "Leonberg", "Lhasa", | |
| # "Maltese_Dog", "Mexican_Hairless", "Newfoundland", "Norfolk_Terrier", "Norwegian_Elkhound", | |
| # "Norwich_Terrier", "Old_English_Sheepdog", "Pekinese", "Pembroke", "Pomeranian", | |
| # "Rhodesian_Ridgeback", "Rottweiler", "Saint_Bernard", "Saluki", "Samoyed", | |
| # "Scotch_Terrier", "Scottish_Deerhound", "Sealyham_Terrier", "Shetland_Sheepdog", "Shiba_Inu", | |
| # "Shih-Tzu", "Siberian_Husky", "Staffordshire_Bullterrier", "Sussex_Spaniel", | |
| # "Tibetan_Mastiff", "Tibetan_Terrier", "Walker_Hound", "Weimaraner", | |
| # "Welsh_Springer_Spaniel", "West_Highland_White_Terrier", "Yorkshire_Terrier", | |
| # "Affenpinscher", "Basenji", "Basset", "Beagle", "Black-and-Tan_Coonhound", "Bloodhound", | |
| # "Bluetick", "Borzoi", "Boxer", "Briard", "Bull_Mastiff", "Cairn", "Chow", "Clumber", | |
| # "Cocker_Spaniel", "Collie", "Curly-Coated_Retriever", "Dhole", "Dingo", | |
| # "Flat-Coated_Retriever", "Giant_Schnauzer", "Golden_Retriever", "Groenendael", "Keeshond", | |
| # "Kelpie", "Komondor", "Kuvasz", "Malamute", "Malinois", "Miniature_Pinscher", | |
| # "Miniature_Poodle", "Miniature_Schnauzer", "Otterhound", "Papillon", "Pug", "Redbone", | |
| # "Schipperke", "Silky_Terrier", "Soft-Coated_Wheaten_Terrier", "Standard_Poodle", | |
| # "Standard_Schnauzer", "Toy_Poodle", "Toy_Terrier", "Vizsla", "Whippet", | |
| # "Wire-Haired_Fox_Terrier"] | |
| # class MultiHeadAttention(nn.Module): | |
| # def __init__(self, in_dim, num_heads=8): | |
| # super().__init__() | |
| # self.num_heads = num_heads | |
| # self.head_dim = max(1, in_dim // num_heads) | |
| # self.scaled_dim = self.head_dim * num_heads | |
| # self.fc_in = nn.Linear(in_dim, self.scaled_dim) | |
| # self.query = nn.Linear(self.scaled_dim, self.scaled_dim) | |
| # self.key = nn.Linear(self.scaled_dim, self.scaled_dim) | |
| # self.value = nn.Linear(self.scaled_dim, self.scaled_dim) | |
| # self.fc_out = nn.Linear(self.scaled_dim, in_dim) | |
| # def forward(self, x): | |
| # N = x.shape[0] | |
| # x = self.fc_in(x) | |
| # q = self.query(x).view(N, self.num_heads, self.head_dim) | |
| # k = self.key(x).view(N, self.num_heads, self.head_dim) | |
| # v = self.value(x).view(N, self.num_heads, self.head_dim) | |
| # energy = torch.einsum("nqd,nkd->nqk", [q, k]) | |
| # attention = F.softmax(energy / (self.head_dim ** 0.5), dim=2) | |
| # out = torch.einsum("nqk,nvd->nqd", [attention, v]) | |
| # out = out.reshape(N, self.scaled_dim) | |
| # out = self.fc_out(out) | |
| # return out | |
| # class BaseModel(nn.Module): | |
| # def __init__(self, num_classes, device='cuda' if torch.cuda.is_available() else 'cpu'): | |
| # super().__init__() | |
| # self.device = device | |
| # self.backbone = efficientnet_v2_m(weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1) | |
| # self.feature_dim = self.backbone.classifier[1].in_features | |
| # self.backbone.classifier = nn.Identity() | |
| # self.num_heads = max(1, min(8, self.feature_dim // 64)) | |
| # self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads) | |
| # self.classifier = nn.Sequential( | |
| # nn.LayerNorm(self.feature_dim), | |
| # nn.Dropout(0.3), | |
| # nn.Linear(self.feature_dim, num_classes) | |
| # ) | |
| # self.to(device) | |
| # def forward(self, x): | |
| # x = x.to(self.device) | |
| # features = self.backbone(x) | |
| # attended_features = self.attention(features) | |
| # logits = self.classifier(attended_features) | |
| # return logits, attended_features | |
| # # Initialize model | |
| # num_classes = len(dog_breeds) | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # # Initialize base model | |
| # model = BaseModel(num_classes=num_classes, device=device).to(device) | |
| # # Load model path | |
| # model_path = "124_best_model_dog.pth" | |
| # checkpoint = torch.load(model_path, map_location=device) | |
| # # Load model state | |
| # model.load_state_dict(checkpoint["base_model"], strict=False) | |
| # model.eval() | |
| # # Image preprocessing function | |
| # def preprocess_image(image): | |
| # # If the image is numpy.ndarray turn into PIL.Image | |
| # if isinstance(image, np.ndarray): | |
| # image = Image.fromarray(image) | |
| # # Use torchvision.transforms to process images | |
| # transform = transforms.Compose([ | |
| # transforms.Resize((224, 224)), | |
| # transforms.ToTensor(), | |
| # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| # ]) | |
| # return transform(image).unsqueeze(0) | |
| # async def predict_single_dog(image): | |
| # """ | |
| # Predicts the dog breed using only the classifier. | |
| # Args: | |
| # image: PIL Image or numpy array | |
| # Returns: | |
| # tuple: (top1_prob, topk_breeds, relative_probs) | |
| # """ | |
| # image_tensor = preprocess_image(image).to(device) | |
| # with torch.no_grad(): | |
| # # Get model outputs (只使用logits，不需要features) | |
| # logits = model(image_tensor)[0] # 如果model仍返回tuple，取第一個元素 | |
| # probs = F.softmax(logits, dim=1) | |
| # # Classifier prediction | |
| # top5_prob, top5_idx = torch.topk(probs, k=5) | |
| # breeds = [dog_breeds[idx.item()] for idx in top5_idx[0]] | |
| # probabilities = [prob.item() for prob in top5_prob[0]] | |
| # # Calculate relative probabilities | |
| # sum_probs = sum(probabilities[:3]) # 只取前三個來計算相對概率 | |
| # relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in probabilities[:3]] | |
| # # Debug output | |
| # print("\nClassifier Predictions:") | |
| # for breed, prob in zip(breeds[:5], probabilities[:5]): | |
| # print(f"{breed}: {prob:.4f}") | |
| # return probabilities[0], breeds[:3], relative_probs | |
| # async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55): | |
| # results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0] | |
| # dogs = [] | |
| # boxes = [] | |
| # for box in results.boxes: | |
| # if box.cls == 16: # COCO dataset class for dog is 16 | |
| # xyxy = box.xyxy[0].tolist() | |
| # confidence = box.conf.item() | |
| # boxes.append((xyxy, confidence)) | |
| # if not boxes: | |
| # dogs.append((image, 1.0, [0, 0, image.width, image.height])) | |
| # else: | |
| # nms_boxes = non_max_suppression(boxes, iou_threshold) | |
| # for box, confidence in nms_boxes: | |
| # x1, y1, x2, y2 = box | |
| # w, h = x2 - x1, y2 - y1 | |
| # x1 = max(0, x1 - w * 0.05) | |
| # y1 = max(0, y1 - h * 0.05) | |
| # x2 = min(image.width, x2 + w * 0.05) | |
| # y2 = min(image.height, y2 + h * 0.05) | |
| # cropped_image = image.crop((x1, y1, x2, y2)) | |
| # dogs.append((cropped_image, confidence, [x1, y1, x2, y2])) | |
| # return dogs | |
| # def non_max_suppression(boxes, iou_threshold): | |
| # keep = [] | |
| # boxes = sorted(boxes, key=lambda x: x[1], reverse=True) | |
| # while boxes: | |
| # current = boxes.pop(0) | |
| # keep.append(current) | |
| # boxes = [box for box in boxes if calculate_iou(current[0], box[0]) < iou_threshold] | |
| # return keep | |
| # def calculate_iou(box1, box2): | |
| # x1 = max(box1[0], box2[0]) | |
| # y1 = max(box1[1], box2[1]) | |
| # x2 = min(box1[2], box2[2]) | |
| # y2 = min(box1[3], box2[3]) | |
| # intersection = max(0, x2 - x1) * max(0, y2 - y1) | |
| # area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) | |
| # area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) | |
| # iou = intersection / float(area1 + area2 - intersection) | |
| # return iou | |
| # def create_breed_comparison(breed1: str, breed2: str) -> dict: | |
| # breed1_info = get_dog_description(breed1) | |
| # breed2_info = get_dog_description(breed2) | |
| # # 標準化數值轉換 | |
| # value_mapping = { | |
| # 'Size': {'Small': 1, 'Medium': 2, 'Large': 3, 'Giant': 4}, | |
| # 'Exercise_Needs': {'Low': 1, 'Moderate': 2, 'High': 3, 'Very High': 4}, | |
| # 'Care_Level': {'Low': 1, 'Moderate': 2, 'High': 3}, | |
| # 'Grooming_Needs': {'Low': 1, 'Moderate': 2, 'High': 3} | |
| # } | |
| # comparison_data = { | |
| # breed1: {}, | |
| # breed2: {} | |
| # } | |
| # for breed, info in [(breed1, breed1_info), (breed2, breed2_info)]: | |
| # comparison_data[breed] = { | |
| # 'Size': value_mapping['Size'].get(info['Size'], 2), # 預設 Medium | |
| # 'Exercise_Needs': value_mapping['Exercise_Needs'].get(info['Exercise Needs'], 2), # 預設 Moderate | |
| # 'Care_Level': value_mapping['Care_Level'].get(info['Care Level'], 2), | |
| # 'Grooming_Needs': value_mapping['Grooming_Needs'].get(info['Grooming Needs'], 2), | |
| # 'Good_with_Children': info['Good with Children'] == 'Yes', | |
| # 'Original_Data': info | |
| # } | |
| # return comparison_data | |
| # async def predict(image): | |
| # """ | |
| # Main prediction function that handles both single and multiple dog detection. | |
| # Args: | |
| # image: PIL Image or numpy array | |
| # Returns: | |
| # tuple: (html_output, annotated_image, initial_state) | |
| # """ | |
| # if image is None: | |
| # return format_warning_html("Please upload an image to start."), None, None | |
| # try: | |
| # if isinstance(image, np.ndarray): | |
| # image = Image.fromarray(image) | |
| # # Detect dogs in the image | |
| # dogs = await detect_multiple_dogs(image) | |
| # color_scheme = get_color_scheme(len(dogs) == 1) | |
| # # Prepare for annotation | |
| # annotated_image = image.copy() | |
| # draw = ImageDraw.Draw(annotated_image) | |
| # try: | |
| # font = ImageFont.truetype("arial.ttf", 24) | |
| # except: | |
| # font = ImageFont.load_default() | |
| # dogs_info = "" | |
| # # Process each detected dog | |
| # for i, (cropped_image, detection_confidence, box) in enumerate(dogs): | |
| # color = color_scheme if len(dogs) == 1 else color_scheme[i % len(color_scheme)] | |
| # # Draw box and label on image | |
| # draw.rectangle(box, outline=color, width=4) | |
| # label = f"Dog {i+1}" | |
| # label_bbox = draw.textbbox((0, 0), label, font=font) | |
| # label_width = label_bbox[2] - label_bbox[0] | |
| # label_height = label_bbox[3] - label_bbox[1] | |
| # # Draw label background and text | |
| # label_x = box[0] + 5 | |
| # label_y = box[1] + 5 | |
| # draw.rectangle( | |
| # [label_x - 2, label_y - 2, label_x + label_width + 4, label_y + label_height + 4], | |
| # fill='white', | |
| # outline=color, | |
| # width=2 | |
| # ) | |
| # draw.text((label_x, label_y), label, fill=color, font=font) | |
| # # Predict breed | |
| # top1_prob, topk_breeds, relative_probs = await predict_single_dog(cropped_image) | |
| # combined_confidence = detection_confidence * top1_prob | |
| # # Format results based on confidence with error handling | |
| # try: | |
| # if combined_confidence < 0.2: | |
| # dogs_info += format_error_message(color, i+1) | |
| # elif top1_prob >= 0.45: | |
| # breed = topk_breeds[0] | |
| # description = get_dog_description(breed) | |
| # # Handle missing breed description | |
| # if description is None: | |
| # # 如果沒有描述，創建一個基本描述 | |
| # description = { | |
| # "Name": breed, | |
| # "Size": "Unknown", | |
| # "Exercise Needs": "Unknown", | |
| # "Grooming Needs": "Unknown", | |
| # "Care Level": "Unknown", | |
| # "Good with Children": "Unknown", | |
| # "Description": f"Identified as {breed.replace('_', ' ')}" | |
| # } | |
| # dogs_info += format_single_dog_result(breed, description, color) | |
| # else: | |
| # # 修改format_multiple_breeds_result的調用，包含錯誤處理 | |
| # dogs_info += format_multiple_breeds_result( | |
| # topk_breeds, | |
| # relative_probs, | |
| # color, | |
| # i+1, | |
| # lambda breed: get_dog_description(breed) or { | |
| # "Name": breed, | |
| # "Size": "Unknown", | |
| # "Exercise Needs": "Unknown", | |
| # "Grooming Needs": "Unknown", | |
| # "Care Level": "Unknown", | |
| # "Good with Children": "Unknown", | |
| # "Description": f"Identified as {breed.replace('_', ' ')}" | |
| # } | |
| # ) | |
| # except Exception as e: | |
| # print(f"Error formatting results for dog {i+1}: {str(e)}") | |
| # dogs_info += format_error_message(color, i+1) | |
| # # Wrap final HTML output | |
| # html_output = format_multi_dog_container(dogs_info) | |
| # # Prepare initial state | |
| # initial_state = { | |
| # "dogs_info": dogs_info, | |
| # "image": annotated_image, | |
| # "is_multi_dog": len(dogs) > 1, | |
| # "html_output": html_output | |
| # } | |
| # return html_output, annotated_image, initial_state | |
| # except Exception as e: | |
| # error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}" | |
| # print(error_msg) | |
| # return format_warning_html(error_msg), None, None | |
| # def show_details_html(choice, previous_output, initial_state): | |
| # """ | |
| # Generate detailed HTML view for a selected breed. | |
| # Args: | |
| # choice: str, Selected breed option | |
| # previous_output: str, Previous HTML output | |
| # initial_state: dict, Current state information | |
| # Returns: | |
| # tuple: (html_output, gradio_update, updated_state) | |
| # """ | |
| # if not choice: | |
| # return previous_output, gr.update(visible=True), initial_state | |
| # try: | |
| # breed = choice.split("More about ")[-1] | |
| # description = get_dog_description(breed) | |
| # html_output = format_breed_details_html(description, breed) | |
| # # Update state | |
| # initial_state["current_description"] = html_output | |
| # initial_state["original_buttons"] = initial_state.get("buttons", []) | |
| # return html_output, gr.update(visible=True), initial_state | |
| # except Exception as e: | |
| # error_msg = f"An error occurred while showing details: {e}" | |
| # print(error_msg) | |
| # return format_warning_html(error_msg), gr.update(visible=True), initial_state | |
| # def main(): | |
| # with gr.Blocks(css=get_css_styles()) as iface: | |
| # # Header HTML | |
| # gr.HTML(""" | |
| # <header style='text-align: center; padding: 20px; margin-bottom: 20px;'> | |
| # <h1 style='font-size: 2.5em; margin-bottom: 10px; color: #2D3748;'> | |
| # 🐾 PawMatch AI | |
| # </h1> | |
| # <h2 style='font-size: 1.2em; font-weight: normal; color: #4A5568; margin-top: 5px;'> | |
| # Your Smart Dog Breed Guide | |
| # </h2> | |
| # <div style='width: 50px; height: 3px; background: linear-gradient(90deg, #4299e1, #48bb78); margin: 15px auto;'></div> | |
| # <p style='color: #718096; font-size: 0.9em;'> | |
| # Powered by AI • Breed Recognition • Smart Matching • Companion Guide | |
| # </p> | |
| # </header> | |
| # """) | |
| # # 先創建歷史組件實例（但不創建標籤頁） | |
| # history_component = create_history_component() | |
| # with gr.Tabs(): | |
| # # 1. 品種檢測標籤頁 | |
| # example_images = [ | |
| # 'Border_Collie.jpg', | |
| # 'Golden_Retriever.jpeg', | |
| # 'Saint_Bernard.jpeg', | |
| # 'Samoyed.jpg', | |
| # 'French_Bulldog.jpeg' | |
| # ] | |
| # detection_components = create_detection_tab(predict, example_images) | |
| # # 2. 品種比較標籤頁 | |
| # comparison_components = create_comparison_tab( | |
| # dog_breeds=dog_breeds, | |
| # get_dog_description=get_dog_description, | |
| # breed_health_info=breed_health_info, | |
| # breed_noise_info=breed_noise_info | |
| # ) | |
| # # 3. 品種推薦標籤頁 | |
| # recommendation_components = create_recommendation_tab( | |
| # UserPreferences=UserPreferences, | |
| # get_breed_recommendations=get_breed_recommendations, | |
| # format_recommendation_html=format_recommendation_html, | |
| # history_component=history_component | |
| # ) | |
| # # 4. 最後創建歷史記錄標籤頁 | |
| # create_history_tab(history_component) | |
| # # Footer | |
| # gr.HTML(''' | |
| # <div style=" | |
| # display: flex; | |
| # align-items: center; | |
| # justify-content: center; | |
| # gap: 20px; | |
| # padding: 20px 0; | |
| # "> | |
| # <p style=" | |
| # font-family: 'Arial', sans-serif; | |
| # font-size: 14px; | |
| # font-weight: 500; | |
| # letter-spacing: 2px; | |
| # background: linear-gradient(90deg, #555, #007ACC); | |
| # -webkit-background-clip: text; | |
| # -webkit-text-fill-color: transparent; | |
| # margin: 0; | |
| # text-transform: uppercase; | |
| # display: inline-block; | |
| # ">EXPLORE THE CODE →</p> | |
| # <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/PawMatchAI" style="text-decoration: none;"> | |
| # <img src="https://img.shields.io/badge/GitHub-PawMatch_AI-007ACC?logo=github&style=for-the-badge"> | |
| # </a> | |
| # </div> | |
| # ''') | |
| # return iface | |
| # if __name__ == "__main__": | |
| # iface = main() | |
| # iface.launch() | |
| import os | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import gradio as gr | |
| import time | |
| from torchvision.models import efficientnet_v2_m, EfficientNet_V2_M_Weights | |
| from torchvision.ops import nms, box_iou | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| from PIL import Image, ImageDraw, ImageFont, ImageFilter | |
| from breed_health_info import breed_health_info | |
| from breed_noise_info import breed_noise_info | |
| from dog_database import get_dog_description | |
| from scoring_calculation_system import UserPreferences | |
| from recommendation_html_format import format_recommendation_html, get_breed_recommendations | |
| from history_manager import UserHistoryManager | |
| from search_history import create_history_tab, create_history_component | |
| from styles import get_css_styles | |
| from breed_detection import create_detection_tab | |
| from breed_comparison import create_comparison_tab | |
| from breed_recommendation import create_recommendation_tab | |
| from html_templates import ( | |
| format_description_html, | |
| format_single_dog_result, | |
| format_multiple_breeds_result, | |
| format_error_message, | |
| format_warning_html, | |
| format_multi_dog_container, | |
| format_breed_details_html, | |
| get_color_scheme, | |
| get_akc_breeds_link | |
| ) | |
| from urllib.parse import quote | |
| from ultralytics import YOLO | |
| import asyncio | |
| import traceback | |
| def get_device(): | |
| if torch.cuda.is_available(): | |
| print('Using CUDA GPU') | |
| return torch.device('cuda') | |
| else: | |
| print('Using CPU') | |
| return torch.device('cpu') | |
| device = get_device() | |
| history_manager = UserHistoryManager() | |
| dog_breeds = ["Afghan_Hound", "African_Hunting_Dog", "Airedale", "American_Staffordshire_Terrier", | |
| "Appenzeller", "Australian_Terrier", "Bedlington_Terrier", "Bernese_Mountain_Dog", "Bichon_Frise", | |
| "Blenheim_Spaniel", "Border_Collie", "Border_Terrier", "Boston_Bull", "Bouvier_Des_Flandres", | |
| "Brabancon_Griffon", "Brittany_Spaniel", "Cardigan", "Chesapeake_Bay_Retriever", | |
| "Chihuahua", "Dachshund", "Dandie_Dinmont", "Doberman", "English_Foxhound", "English_Setter", | |
| "English_Springer", "EntleBucher", "Eskimo_Dog", "French_Bulldog", "German_Shepherd", | |
| "German_Short-Haired_Pointer", "Gordon_Setter", "Great_Dane", "Great_Pyrenees", | |
| "Greater_Swiss_Mountain_Dog","Havanese", "Ibizan_Hound", "Irish_Setter", "Irish_Terrier", | |
| "Irish_Water_Spaniel", "Irish_Wolfhound", "Italian_Greyhound", "Japanese_Spaniel", | |
| "Kerry_Blue_Terrier", "Labrador_Retriever", "Lakeland_Terrier", "Leonberg", "Lhasa", | |
| "Maltese_Dog", "Mexican_Hairless", "Newfoundland", "Norfolk_Terrier", "Norwegian_Elkhound", | |
| "Norwich_Terrier", "Old_English_Sheepdog", "Pekinese", "Pembroke", "Pomeranian", | |
| "Rhodesian_Ridgeback", "Rottweiler", "Saint_Bernard", "Saluki", "Samoyed", | |
| "Scotch_Terrier", "Scottish_Deerhound", "Sealyham_Terrier", "Shetland_Sheepdog", "Shiba_Inu", | |
| "Shih-Tzu", "Siberian_Husky", "Staffordshire_Bullterrier", "Sussex_Spaniel", | |
| "Tibetan_Mastiff", "Tibetan_Terrier", "Walker_Hound", "Weimaraner", | |
| "Welsh_Springer_Spaniel", "West_Highland_White_Terrier", "Yorkshire_Terrier", | |
| "Affenpinscher", "Basenji", "Basset", "Beagle", "Black-and-Tan_Coonhound", "Bloodhound", | |
| "Bluetick", "Borzoi", "Boxer", "Briard", "Bull_Mastiff", "Cairn", "Chow", "Clumber", | |
| "Cocker_Spaniel", "Collie", "Curly-Coated_Retriever", "Dhole", "Dingo", | |
| "Flat-Coated_Retriever", "Giant_Schnauzer", "Golden_Retriever", "Groenendael", "Keeshond", | |
| "Kelpie", "Komondor", "Kuvasz", "Malamute", "Malinois", "Miniature_Pinscher", | |
| "Miniature_Poodle", "Miniature_Schnauzer", "Otterhound", "Papillon", "Pug", "Redbone", | |
| "Schipperke", "Silky_Terrier", "Soft-Coated_Wheaten_Terrier", "Standard_Poodle", | |
| "Standard_Schnauzer", "Toy_Poodle", "Toy_Terrier", "Vizsla", "Whippet", | |
| "Wire-Haired_Fox_Terrier"] | |
| class MultiHeadAttention(nn.Module): | |
| def __init__(self, in_dim, num_heads=8): | |
| super().__init__() | |
| self.num_heads = num_heads | |
| self.head_dim = max(1, in_dim // num_heads) | |
| self.scaled_dim = self.head_dim * num_heads | |
| self.fc_in = nn.Linear(in_dim, self.scaled_dim) | |
| self.query = nn.Linear(self.scaled_dim, self.scaled_dim) | |
| self.key = nn.Linear(self.scaled_dim, self.scaled_dim) | |
| self.value = nn.Linear(self.scaled_dim, self.scaled_dim) | |
| self.fc_out = nn.Linear(self.scaled_dim, in_dim) | |
| def forward(self, x): | |
| N = x.shape[0] | |
| x = self.fc_in(x) | |
| q = self.query(x).view(N, self.num_heads, self.head_dim) | |
| k = self.key(x).view(N, self.num_heads, self.head_dim) | |
| v = self.value(x).view(N, self.num_heads, self.head_dim) | |
| energy = torch.einsum("nqd,nkd->nqk", [q, k]) | |
| attention = F.softmax(energy / (self.head_dim ** 0.5), dim=2) | |
| out = torch.einsum("nqk,nvd->nqd", [attention, v]) | |
| out = out.reshape(N, self.scaled_dim) | |
| out = self.fc_out(out) | |
| return out | |
| class BaseModel(nn.Module): | |
| def __init__(self, num_classes, device=None): | |
| super().__init__() | |
| if device is None: | |
| device = get_device() | |
| self.device = device | |
| print(f"Initializing model on device: {device}") | |
| self.backbone = efficientnet_v2_m(weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1) | |
| self.feature_dim = self.backbone.classifier[1].in_features | |
| self.backbone.classifier = nn.Identity() | |
| self.num_heads = max(1, min(8, self.feature_dim // 64)) | |
| self.attention = MultiHeadAttention(self.feature_dim, num_heads=self.num_heads) | |
| self.classifier = nn.Sequential( | |
| nn.LayerNorm(self.feature_dim), | |
| nn.Dropout(0.3), | |
| nn.Linear(self.feature_dim, num_classes) | |
| ) | |
| self.to(device) | |
| def forward(self, x): | |
| x = x.to(self.device) | |
| features = self.backbone(x) | |
| attended_features = self.attention(features) | |
| logits = self.classifier(attended_features) | |
| return logits, attended_features | |
| # Initialize model | |
| num_classes = len(dog_breeds) | |
| # Initialize base model | |
| model = BaseModel(num_classes=num_classes, device=device) | |
| # Load model path | |
| model_path = '/content/drive/Othercomputers/我的 MacBook Pro/Learning/Cats_Dogs_Detector/(124_TEST)_models/[124_82.30]_best_model_dog.pth' | |
| checkpoint = torch.load(model_path, map_location=device) | |
| # Load model state | |
| model.load_state_dict(checkpoint['base_model'], strict=False) | |
| model.eval() | |
| # Image preprocessing function | |
| def preprocess_image(image): | |
| # If the image is numpy.ndarray turn into PIL.Image | |
| if isinstance(image, np.ndarray): | |
| image = Image.fromarray(image) | |
| # Use torchvision.transforms to process images | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| ]) | |
| return transform(image).unsqueeze(0) | |
| model_yolo = YOLO('yolov8l.pt') | |
| if torch.cuda.is_available(): | |
| model_yolo.to(device) | |
| async def predict_single_dog(image): | |
| """ | |
| Predicts the dog breed using only the classifier. | |
| Args: | |
| image: PIL Image or numpy array | |
| Returns: | |
| tuple: (top1_prob, topk_breeds, relative_probs) | |
| """ | |
| image_tensor = preprocess_image(image).to(device) | |
| with torch.no_grad(): | |
| # Get model outputs (只使用logits，不需要features) | |
| logits = model(image_tensor)[0] # 如果model仍返回tuple，取第一個元素 | |
| probs = F.softmax(logits, dim=1) | |
| # Classifier prediction | |
| top5_prob, top5_idx = torch.topk(probs, k=5) | |
| breeds = [dog_breeds[idx.item()] for idx in top5_idx[0]] | |
| probabilities = [prob.item() for prob in top5_prob[0]] | |
| # Calculate relative probabilities | |
| sum_probs = sum(probabilities[:3]) # 只取前三個來計算相對概率 | |
| relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in probabilities[:3]] | |
| # Debug output | |
| print("\nClassifier Predictions:") | |
| for breed, prob in zip(breeds[:5], probabilities[:5]): | |
| print(f"{breed}: {prob:.4f}") | |
| return probabilities[0], breeds[:3], relative_probs | |
| async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.55): | |
| results = model_yolo(image, conf=conf_threshold, iou=iou_threshold)[0] | |
| dogs = [] | |
| boxes = [] | |
| for box in results.boxes: | |
| if box.cls == 16: # COCO dataset class for dog is 16 | |
| xyxy = box.xyxy[0].tolist() | |
| confidence = box.conf.item() | |
| boxes.append((xyxy, confidence)) | |
| if not boxes: | |
| dogs.append((image, 1.0, [0, 0, image.width, image.height])) | |
| else: | |
| nms_boxes = non_max_suppression(boxes, iou_threshold) | |
| for box, confidence in nms_boxes: | |
| x1, y1, x2, y2 = box | |
| w, h = x2 - x1, y2 - y1 | |
| x1 = max(0, x1 - w * 0.05) | |
| y1 = max(0, y1 - h * 0.05) | |
| x2 = min(image.width, x2 + w * 0.05) | |
| y2 = min(image.height, y2 + h * 0.05) | |
| cropped_image = image.crop((x1, y1, x2, y2)) | |
| dogs.append((cropped_image, confidence, [x1, y1, x2, y2])) | |
| return dogs | |
| def non_max_suppression(boxes, iou_threshold): | |
| keep = [] | |
| boxes = sorted(boxes, key=lambda x: x[1], reverse=True) | |
| while boxes: | |
| current = boxes.pop(0) | |
| keep.append(current) | |
| boxes = [box for box in boxes if calculate_iou(current[0], box[0]) < iou_threshold] | |
| return keep | |
| def calculate_iou(box1, box2): | |
| x1 = max(box1[0], box2[0]) | |
| y1 = max(box1[1], box2[1]) | |
| x2 = min(box1[2], box2[2]) | |
| y2 = min(box1[3], box2[3]) | |
| intersection = max(0, x2 - x1) * max(0, y2 - y1) | |
| area1 = (box1[2] - box1[0]) * (box1[3] - box1[1]) | |
| area2 = (box2[2] - box2[0]) * (box2[3] - box2[1]) | |
| iou = intersection / float(area1 + area2 - intersection) | |
| return iou | |
| def create_breed_comparison(breed1: str, breed2: str) -> dict: | |
| breed1_info = get_dog_description(breed1) | |
| breed2_info = get_dog_description(breed2) | |
| # 標準化數值轉換 | |
| value_mapping = { | |
| 'Size': {'Small': 1, 'Medium': 2, 'Large': 3, 'Giant': 4}, | |
| 'Exercise_Needs': {'Low': 1, 'Moderate': 2, 'High': 3, 'Very High': 4}, | |
| 'Care_Level': {'Low': 1, 'Moderate': 2, 'High': 3}, | |
| 'Grooming_Needs': {'Low': 1, 'Moderate': 2, 'High': 3} | |
| } | |
| comparison_data = { | |
| breed1: {}, | |
| breed2: {} | |
| } | |
| for breed, info in [(breed1, breed1_info), (breed2, breed2_info)]: | |
| comparison_data[breed] = { | |
| 'Size': value_mapping['Size'].get(info['Size'], 2), # 預設 Medium | |
| 'Exercise_Needs': value_mapping['Exercise_Needs'].get(info['Exercise Needs'], 2), # 預設 Moderate | |
| 'Care_Level': value_mapping['Care_Level'].get(info['Care Level'], 2), | |
| 'Grooming_Needs': value_mapping['Grooming_Needs'].get(info['Grooming Needs'], 2), | |
| 'Good_with_Children': info['Good with Children'] == 'Yes', | |
| 'Original_Data': info | |
| } | |
| return comparison_data | |
| async def predict(image): | |
| """ | |
| Main prediction function that handles both single and multiple dog detection. | |
| Args: | |
| image: PIL Image or numpy array | |
| Returns: | |
| tuple: (html_output, annotated_image, initial_state) | |
| """ | |
| if image is None: | |
| return format_warning_html("Please upload an image to start."), None, None | |
| try: | |
| if isinstance(image, np.ndarray): | |
| image = Image.fromarray(image) | |
| # Detect dogs in the image | |
| dogs = await detect_multiple_dogs(image) | |
| color_scheme = get_color_scheme(len(dogs) == 1) | |
| # Prepare for annotation | |
| annotated_image = image.copy() | |
| draw = ImageDraw.Draw(annotated_image) | |
| try: | |
| font = ImageFont.truetype("arial.ttf", 24) | |
| except: | |
| font = ImageFont.load_default() | |
| dogs_info = "" | |
| # Process each detected dog | |
| for i, (cropped_image, detection_confidence, box) in enumerate(dogs): | |
| color = color_scheme if len(dogs) == 1 else color_scheme[i % len(color_scheme)] | |
| # Draw box and label on image | |
| draw.rectangle(box, outline=color, width=4) | |
| label = f"Dog {i+1}" | |
| label_bbox = draw.textbbox((0, 0), label, font=font) | |
| label_width = label_bbox[2] - label_bbox[0] | |
| label_height = label_bbox[3] - label_bbox[1] | |
| # Draw label background and text | |
| label_x = box[0] + 5 | |
| label_y = box[1] + 5 | |
| draw.rectangle( | |
| [label_x - 2, label_y - 2, label_x + label_width + 4, label_y + label_height + 4], | |
| fill='white', | |
| outline=color, | |
| width=2 | |
| ) | |
| draw.text((label_x, label_y), label, fill=color, font=font) | |
| # Predict breed | |
| top1_prob, topk_breeds, relative_probs = await predict_single_dog(cropped_image) | |
| combined_confidence = detection_confidence * top1_prob | |
| # Format results based on confidence with error handling | |
| try: | |
| if combined_confidence < 0.2: | |
| dogs_info += format_error_message(color, i+1) | |
| elif top1_prob >= 0.45: | |
| breed = topk_breeds[0] | |
| description = get_dog_description(breed) | |
| # Handle missing breed description | |
| if description is None: | |
| # 如果沒有描述，創建一個基本描述 | |
| description = { | |
| "Name": breed, | |
| "Size": "Unknown", | |
| "Exercise Needs": "Unknown", | |
| "Grooming Needs": "Unknown", | |
| "Care Level": "Unknown", | |
| "Good with Children": "Unknown", | |
| "Description": f"Identified as {breed.replace('_', ' ')}" | |
| } | |
| dogs_info += format_single_dog_result(breed, description, color) | |
| else: | |
| # 修改format_multiple_breeds_result的調用，包含錯誤處理 | |
| dogs_info += format_multiple_breeds_result( | |
| topk_breeds, | |
| relative_probs, | |
| color, | |
| i+1, | |
| lambda breed: get_dog_description(breed) or { | |
| "Name": breed, | |
| "Size": "Unknown", | |
| "Exercise Needs": "Unknown", | |
| "Grooming Needs": "Unknown", | |
| "Care Level": "Unknown", | |
| "Good with Children": "Unknown", | |
| "Description": f"Identified as {breed.replace('_', ' ')}" | |
| } | |
| ) | |
| except Exception as e: | |
| print(f"Error formatting results for dog {i+1}: {str(e)}") | |
| dogs_info += format_error_message(color, i+1) | |
| # Wrap final HTML output | |
| html_output = format_multi_dog_container(dogs_info) | |
| # Prepare initial state | |
| initial_state = { | |
| "dogs_info": dogs_info, | |
| "image": annotated_image, | |
| "is_multi_dog": len(dogs) > 1, | |
| "html_output": html_output | |
| } | |
| return html_output, annotated_image, initial_state | |
| except Exception as e: | |
| error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}" | |
| print(error_msg) | |
| return format_warning_html(error_msg), None, None | |
| def show_details_html(choice, previous_output, initial_state): | |
| """ | |
| Generate detailed HTML view for a selected breed. | |
| Args: | |
| choice: str, Selected breed option | |
| previous_output: str, Previous HTML output | |
| initial_state: dict, Current state information | |
| Returns: | |
| tuple: (html_output, gradio_update, updated_state) | |
| """ | |
| if not choice: | |
| return previous_output, gr.update(visible=True), initial_state | |
| try: | |
| breed = choice.split("More about ")[-1] | |
| description = get_dog_description(breed) | |
| html_output = format_breed_details_html(description, breed) | |
| # Update state | |
| initial_state["current_description"] = html_output | |
| initial_state["original_buttons"] = initial_state.get("buttons", []) | |
| return html_output, gr.update(visible=True), initial_state | |
| except Exception as e: | |
| error_msg = f"An error occurred while showing details: {e}" | |
| print(error_msg) | |
| return format_warning_html(error_msg), gr.update(visible=True), initial_state | |
| def main(): | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| with gr.Blocks(css=get_css_styles()) as iface: | |
| # Header HTML | |
| gr.HTML(""" | |
| <header style='text-align: center; padding: 20px; margin-bottom: 20px;'> | |
| <h1 style='font-size: 2.5em; margin-bottom: 10px; color: #2D3748;'> | |
| 🐾 PawMatch AI | |
| </h1> | |
| <h2 style='font-size: 1.2em; font-weight: normal; color: #4A5568; margin-top: 5px;'> | |
| Your Smart Dog Breed Guide | |
| </h2> | |
| <div style='width: 50px; height: 3px; background: linear-gradient(90deg, #4299e1, #48bb78); margin: 15px auto;'></div> | |
| <p style='color: #718096; font-size: 0.9em;'> | |
| Powered by AI • Breed Recognition • Smart Matching • Companion Guide | |
| </p> | |
| </header> | |
| """) | |
| # 先創建歷史組件實例（但不創建標籤頁） | |
| history_component = create_history_component() | |
| with gr.Tabs(): | |
| # 1. 品種檢測標籤頁 | |
| example_images = [ | |
| '/content/drive/Othercomputers/我的 MacBook Pro/Learning/Cats_Dogs_Detector/test_images/Border_Collie.jpg', | |
| '/content/drive/Othercomputers/我的 MacBook Pro/Learning/Cats_Dogs_Detector/test_images/Golden_Retriever.jpeg', | |
| '/content/drive/Othercomputers/我的 MacBook Pro/Learning/Cats_Dogs_Detector/test_images/Saint_Bernard.jpeg', | |
| '/content/drive/Othercomputers/我的 MacBook Pro/Learning/Cats_Dogs_Detector/test_images/Samoyed.jpg', | |
| '/content/drive/Othercomputers/我的 MacBook Pro/Learning/Cats_Dogs_Detector/test_images/French_Bulldog.jpeg' | |
| ] | |
| detection_components = create_detection_tab(predict, example_images) | |
| # 2. 品種比較標籤頁 | |
| comparison_components = create_comparison_tab( | |
| dog_breeds=dog_breeds, | |
| get_dog_description=get_dog_description, | |
| breed_health_info=breed_health_info, | |
| breed_noise_info=breed_noise_info | |
| ) | |
| # 3. 品種推薦標籤頁 | |
| recommendation_components = create_recommendation_tab( | |
| UserPreferences=UserPreferences, | |
| get_breed_recommendations=get_breed_recommendations, | |
| format_recommendation_html=format_recommendation_html, | |
| history_component=history_component | |
| ) | |
| # 4. 最後創建歷史記錄標籤頁 | |
| create_history_tab(history_component) | |
| # Footer | |
| gr.HTML(''' | |
| <div style=" | |
| display: flex; | |
| align-items: center; | |
| justify-content: center; | |
| gap: 20px; | |
| padding: 20px 0; | |
| "> | |
| <p style=" | |
| font-family: 'Arial', sans-serif; | |
| font-size: 14px; | |
| font-weight: 500; | |
| letter-spacing: 2px; | |
| background: linear-gradient(90deg, #555, #007ACC); | |
| -webkit-background-clip: text; | |
| -webkit-text-fill-color: transparent; | |
| margin: 0; | |
| text-transform: uppercase; | |
| display: inline-block; | |
| ">EXPLORE THE CODE →</p> | |
| <a href="https://github.com/Eric-Chung-0511/Learning-Record/tree/main/Data%20Science%20Projects/PawMatchAI" style="text-decoration: none;"> | |
| <img src="https://img.shields.io/badge/GitHub-PawMatch_AI-007ACC?logo=github&style=for-the-badge"> | |
| </a> | |
| </div> | |
| ''') | |
| return iface | |
| if __name__ == "__main__": | |
| print(f"CUDA available: {torch.cuda.is_available()}") | |
| if torch.cuda.is_available(): | |
| print(f"Current device: {torch.cuda.current_device()}") | |
| print(f"Device name: {torch.cuda.get_device_name()}") | |
| iface = main() | |
| iface.launch(share=True, debug=True) |