import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# Load pre-trained model
model = models.resnet50(pretrained=True)
model.eval()

# Preprocess image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

img = Image.open('image.jpg')
img_tensor = transform(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(img_tensor)
    probabilities = torch.nn.functional.softmax(output[0], dim=0)
    top5 = torch.topk(probabilities, 5)

print(top5)

import torch.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self, num_classes=10):
        super(SimpleCNN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2)
        )
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 4 * 4, 512),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x)
        return x

from torchvision import transforms

train_transform = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(15),
    transforms.ColorJitter(
        brightness=0.2,
        contrast=0.2,
        saturation=0.2,
        hue=0.1
    ),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

from ultralytics import YOLO

# Load model
model = YOLO('yolov8n.pt')

# Predict
results = model('image.jpg')

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        x1, y1, x2, y2 = box.xyxy[0]
        confidence = box.conf[0]
        class_id = box.cls[0]
        print(f"Class: {class_id}, Confidence: {confidence:.2f}")
        print(f"Box: ({x1}, {y1}, {x2}, {y2})")

# Save results
results[0].save('output.jpg')

# Semantic segmentation with DeepLab
model = torch.hub.load(
    'pytorch/vision:v0.10.0',
    'deeplabv3_resnet50',
    pretrained=True
)
model.eval()

# Preprocess
preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225]
    )
])

input_tensor = preprocess(img).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(input_tensor)['out'][0]
    output_predictions = output.argmax(0)

from torchvision import models

# Load pre-trained ResNet
model = models.resnet50(pretrained=True)

# Freeze all layers
for param in model.parameters():
    param.requires_grad = False

# Replace final layer
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, num_classes)

# Train only final layer
optimizer = optim.Adam(model.fc.parameters(), lr=0.001)

import cv2

# Read image
img = cv2.imread('image.jpg')

# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Edge detection
edges = cv2.Canny(gray, 100, 200)

# Blur
blurred = cv2.GaussianBlur(img, (5, 5), 0)

# Resize
resized = cv2.resize(img, (224, 224))

# Draw rectangle
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)

# Save
cv2.imwrite('output.jpg', img)

# Haar Cascade
face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)