import os
import numpy as np
import torch
import torch.nn as nn
import gradio as gr
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 data_manager import get_dog_description
from urllib.parse import quote
from ultralytics import YOLO
import asyncio
import traceback
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
# 下載YOLOv8預訓練模型
model_yolo = YOLO('yolov8s.pt') # 使用 YOLOv8 預訓練模型
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)
def get_akc_breeds_link():
return "https://www.akc.org/dog-breeds/"
def format_description(description, breed):
if isinstance(description, dict):
# 確保每一個描述項目換行顯示
formatted_description = "\n\n".join([f"**{key}**: {value}" for key, value in description.items()])
else:
formatted_description = description
akc_link = get_akc_breeds_link()
formatted_description += f"\n\n**Want to learn more about dog breeds?** [Visit the AKC dog breeds page]({akc_link}) and search for {breed} to find detailed information."
disclaimer = ("\n\n*Disclaimer: The external link provided leads to the American Kennel Club (AKC) dog breeds page. "
"You may need to search for the specific breed on that page. "
"I am not responsible for the content on external sites. "
"Please refer to the AKC's terms of use and privacy policy.*")
formatted_description += disclaimer
return formatted_description
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]]
topk_probs_percent = [f"{prob.item() * 100:.2f}%" for prob in topk_probs[0]]
return top1_prob, topk_breeds, topk_probs_percent
async def detect_multiple_dogs(image, conf_threshold=0.35, 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
async def process_single_dog(image):
top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(image)
if top1_prob < 0.15:
initial_state = {
"explanation": "The image is unclear or the breed is not in the dataset. Please upload a clearer image of a dog.",
"buttons": [],
"show_back": False,
"image": None,
"is_multi_dog": False
}
return initial_state["explanation"], None, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
breed = topk_breeds[0]
description = get_dog_description(breed)
if top1_prob >= 0.45:
formatted_description = format_description(description, breed)
initial_state = {
"explanation": formatted_description,
"buttons": [],
"show_back": False,
"image": image,
"is_multi_dog": False
}
return formatted_description, image, gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), initial_state
else:
explanation = (
f"The model couldn't confidently identify the breed. Here are the top 3 possible breeds:\n\n"
f"1. **{topk_breeds[0]}** ({topk_probs_percent[0]} confidence)\n"
f"2. **{topk_breeds[1]}** ({topk_probs_percent[1]} confidence)\n"
f"3. **{topk_breeds[2]}** ({topk_probs_percent[2]} confidence)\n\n"
"Click on a button to view more information about the breed."
)
buttons = [
gr.update(visible=True, value=f"More about {topk_breeds[0]}"),
gr.update(visible=True, value=f"More about {topk_breeds[1]}"),
gr.update(visible=True, value=f"More about {topk_breeds[2]}")
]
initial_state = {
"explanation": explanation,
"buttons": buttons,
"show_back": True,
"image": image,
"is_multi_dog": False
}
return explanation, image, buttons[0], buttons[1], buttons[2], gr.update(visible=True), initial_state
async def predict(image):
if image is None:
return "Please upload an image to start.", None, gr.update(visible=False, choices=[]), None
try:
if isinstance(image, np.ndarray):
image = Image.fromarray(image)
dogs = await detect_multiple_dogs(image)
color_list = ['#FF0000', '#00FF00', '#0000FF', '#FFFF00', '#00FFFF', '#FF00FF', '#800080', '#FFA500']
explanations = []
buttons = []
annotated_image = image.copy()
draw = ImageDraw.Draw(annotated_image)
font = ImageFont.load_default()
for i, (cropped_image, detection_confidence, box) in enumerate(dogs):
top1_prob, topk_breeds, topk_probs_percent = await predict_single_dog(cropped_image)
color = color_list[i % len(color_list)]
draw.rectangle(box, outline=color, width=3)
draw.text((box[0] + 5, box[1] + 5), f"Dog {i+1}", fill=color, font=font)
combined_confidence = detection_confidence * top1_prob
if top1_prob >= 0.45:
breed = topk_breeds[0]
description = get_dog_description(breed)
formatted_description = format_description(description, breed)
explanations.append(f"Dog {i+1}: {formatted_description}")
elif combined_confidence >= 0.15:
dog_explanation = f"Dog {i+1}: Top 3 possible breeds:\n"
dog_explanation += "\n".join([f"{j+1}. **{breed}** ({prob} confidence)" for j, (breed, prob) in enumerate(zip(topk_breeds[:3], topk_probs_percent[:3]))])
explanations.append(dog_explanation)
buttons.extend([f"Dog {i+1}: More about {breed}" for breed in topk_breeds[:3]])
else:
explanations.append("The image is unclear or the breed is not in the dataset. Please upload a clearer image.")
final_explanation = "\n\n".join(explanations)
if buttons:
final_explanation += "\n\nClick on a button to view more information about the breed."
initial_state = {
"explanation": final_explanation,
"buttons": buttons,
"show_back": True,
"image": annotated_image,
"is_multi_dog": len(dogs) > 1,
"dogs_info": explanations
}
return final_explanation, annotated_image, gr.update(visible=True, choices=buttons), initial_state
else:
initial_state = {
"explanation": final_explanation,
"buttons": [],
"show_back": False,
"image": annotated_image,
"is_multi_dog": len(dogs) > 1,
"dogs_info": explanations
}
return final_explanation, annotated_image, gr.update(visible=False, choices=[]), initial_state
except Exception as e:
error_msg = f"An error occurred: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
print(error_msg)
return error_msg, None, gr.update(visible=False, choices=[]), None
def show_details(choice, previous_output, initial_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)
formatted_description = format_description(description, breed)
initial_state["current_description"] = formatted_description
initial_state["original_buttons"] = initial_state.get("buttons", [])
return formatted_description, gr.update(visible=True), initial_state
except Exception as e:
error_msg = f"An error occurred while showing details: {e}"
print(error_msg)
return error_msg, gr.update(visible=True), initial_state
def go_back(state):
buttons = state.get("buttons", [])
return (
state["explanation"],
state["image"],
gr.update(visible=True, choices=buttons),
gr.update(visible=False),
state
)
with gr.Blocks() as iface:
gr.HTML("🐶 Dog Breed Classifier 🔍
")
gr.HTML("Upload a picture of a dog, and the model will predict its breed, provide detailed information, and include an extra information link!
")
with gr.Row():
input_image = gr.Image(label="Upload a dog image", type="pil")
output_image = gr.Image(label="Annotated Image")
output = gr.Markdown(label="Prediction Results")
breed_buttons = gr.Radio(choices=[], label="More Information", visible=False)
back_button = gr.Button("Back", visible=False)
initial_state = gr.State()
input_image.change(
predict,
inputs=input_image,
outputs=[output, output_image, breed_buttons, initial_state]
)
breed_buttons.change(
show_details,
inputs=[breed_buttons, output, initial_state],
outputs=[output, back_button, initial_state]
)
back_button.click(
go_back,
inputs=[initial_state],
outputs=[output, output_image, breed_buttons, back_button, initial_state]
)
gr.Examples(
examples=['Border_Collie.jpg', 'Golden_Retriever.jpeg', 'Saint_Bernard.jpeg', 'French_Bulldog.jpeg', 'Samoyed.jpg'],
inputs=input_image
)
gr.HTML('For more details on this project and other work, feel free to visit my GitHub Dog Breed Classifier')
if __name__ == "__main__":
iface.launch()