import os
import numpy as np
import torch
import torch.nn as nn
import gradio as gr
from dataclasses import dataclass
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 dog_database import get_dog_description
from breed_health_info import breed_health_info
from breed_noise_info import breed_noise_info
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",
"Blenheim_Spaniel", "Border_Collie", "Border_Terrier", "Boston_Bull", "Bouvier_Des_Flandres",
"Brabancon_Griffon", "Brittany_Spaniel", "Cardigan", "Chesapeake_Bay_Retriever",
"Chihuahua", "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", "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",
"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
num_classes = 120
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = BaseModel(num_classes=num_classes, device=device)
checkpoint = torch.load('best_model_81_dog.pth', map_location=torch.device('cpu'))
model.load_state_dict(checkpoint['model_state_dict'])
# evaluation mode
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):
image_tensor = preprocess_image(image)
with torch.no_grad():
output = model(image_tensor)
logits = output[0] if isinstance(output, tuple) else output
probabilities = F.softmax(logits, dim=1)
topk_probs, topk_indices = torch.topk(probabilities, k=3)
top1_prob = topk_probs[0][0].item()
topk_breeds = [dog_breeds[idx.item()] for idx in topk_indices[0]]
# Calculate relative probabilities for display
raw_probs = [prob.item() for prob in topk_probs[0]]
sum_probs = sum(raw_probs)
relative_probs = [f"{(prob/sum_probs * 100):.2f}%" for prob in raw_probs]
return top1_prob, topk_breeds, relative_probs
async def detect_multiple_dogs(image, conf_threshold=0.3, iou_threshold=0.45):
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
async def process_single_dog(image):
"""Process a single dog image and return breed predictions and HTML output."""
top1_prob, topk_breeds, relative_probs = await predict_single_dog(image)
# Case 1: Low confidence - unclear image or breed not in dataset
if top1_prob < 0.2:
error_message = format_warning_html(
'The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.'
)
initial_state = {
"explanation": error_message,
"image": None,
"is_multi_dog": False
}
return error_message, None, initial_state
breed = topk_breeds[0]
# Case 2: High confidence - single breed result
if top1_prob >= 0.45:
description = get_dog_description(breed)
html_content = format_single_dog_result(breed, description)
initial_state = {
"explanation": html_content,
"image": image,
"is_multi_dog": False
}
return html_content, image, initial_state
# Case 3: Medium confidence - show top 3 breeds with relative probabilities
description = get_dog_description(breed)
breeds_html = format_multiple_breeds_result(
topk_breeds=topk_breeds,
relative_probs=relative_probs,
color='#34C759', # 使用單狗顏色
index=1, # 因為是單狗處理，所以index為1
get_dog_description=get_dog_description
)
initial_state = {
"explanation": breeds_html,
"image": image,
"is_multi_dog": False
}
return breeds_html, image, initial_state
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
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)
dogs_info += format_single_dog_result(breed, description, color)
else:
dogs_info += format_multiple_breeds_result(
topk_breeds,
relative_probs,
color,
i+1,
get_dog_description
)
# 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("""
""")
# 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('''
For more details on this project and other work, feel free to visit my GitHub
Dog Breed Classifier
''')
return iface
if __name__ == "__main__":
iface = main()
iface.launch(share=True, debug=True)