# 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(""" #
#

# 🐾 PawMatch AI #

#

# Your Smart Dog Breed Guide #

#
#

# Powered by AI • Breed Recognition • Smart Matching • Companion Guide #

#
# """) # # 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(''' #
#

EXPLORE THE CODE →

# # # #
# ''') # 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 os.environ['CUDA_VISIBLE_DEVICES'] = '0' os.environ['HF_ZERO_GPU'] = '1' # 明確告訴系統我們要使用 ZeroGPU os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:512' def get_device(): print("Initializing device configuration...") # 特別針對 ZeroGPU 的檢測邏輯 if 'HF_ZERO_GPU' in os.environ and torch.cuda.is_available(): try: # 強制進行 CUDA 初始化 torch.cuda.init() # 等待一小段時間讓系統完成初始化 import time time.sleep(2) device = torch.device('cuda') # 執行一個小的測試來確認 GPU 功能 test_tensor = torch.rand(1).to(device) _ = test_tensor * test_tensor print("ZeroGPU initialization successful") print(f"Using device: {device}") if torch.cuda.is_available(): print(f"GPU: {torch.cuda.get_device_name(0)}") return device except Exception as e: print(f"ZeroGPU initialization failed: {str(e)}") print("Falling back to CPU") return torch.device('cpu') else: if not torch.cuda.is_available(): print("CUDA not available, using CPU") elif 'HF_ZERO_GPU' not in os.environ: print("HF_ZERO_GPU not set, 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).to(self.device) 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).to(self.device) 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): if x.device != self.device: 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 = "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) def initialize_yolo_model(device): try: model_yolo = YOLO('yolov8l.pt') if torch.cuda.is_available(): model_yolo.to(device) print(f"YOLO model initialized on {device}") return model_yolo except Exception as e: print(f"Error initializing YOLO model: {str(e)}") print("Attempting to initialize YOLO model on CPU") return YOLO('yolov8l.pt') model_yolo = initialize_yolo_model(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(): print("\n=== System Information ===") print(f"PyTorch Version: {torch.__version__}") print(f"CUDA Available: {torch.cuda.is_available()}") if torch.cuda.is_available(): print(f"CUDA Version: {torch.version.cuda}") print(f"Current Device: {torch.cuda.current_device()}") # 清理 GPU 記憶體（如果可用） if torch.cuda.is_available(): torch.cuda.empty_cache() device = get_device() with gr.Blocks(css=get_css_styles()) as iface: # Header HTML gr.HTML("""

🐾 PawMatch AI

Your Smart Dog Breed Guide

Powered by AI • Breed Recognition • Smart Matching • Companion Guide

""") # 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('''

EXPLORE THE CODE →

''') 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()