纵观全局：YOLO 助力实时物体检测原理及代码

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

# Load a pre-trained YOLO model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# Function to perform object detection
def detect_objects(image_path):
# Load and transform the image
image = Image.open(image_path)
transform = transforms.Compose([
transforms.ToTensor(),
])
image = transform(image).unsqueeze(0) # Add batch dimension

# Perform inference
model.eval() # Set the model to evaluation mode
with torch.no_grad():
predictions = model(image)

# Process predictions
# Note: The output format can vary, so adjust the processing as needed
for pred in predictions[0]:
bbox = pred[:4] # Bounding box coordinates
score = pred[4] # Confidence score
class_id = pred[5] # Class ID
print(f'Class: {class_id}, Score: {score}, BBox: {bbox}')

# Example usage
detect_objects('path/to/your/image.jpg')

import torch
import torch.optim as optim

# Assuming yolo_model is your YOLO model and train_loader is your data loader

# Define the optimizer
optimizer = optim.Adam(yolo_model.parameters(), lr=0.001)

# Placeholder for the YOLO loss function
# Note: You'll need to define this based on the specific YOLO version and its output format
def yolo_loss(predictions, targets):
    # Compute localization loss, confidence loss, and classification loss
    # localization_loss = ...
    # confidence_loss = ...
    # classification_loss = ...
    
    # Combine the losses
    total_loss = localization_loss + confidence_loss + classification_loss
    return total_loss

# Training loop
for epoch in range(num_epochs):
    for images, targets in train_loader:  # Assuming targets contain ground truth
        optimizer.zero_grad()  # Zero the gradients
        
        # Forward pass
        predictions = yolo_model(images)
        
        # Compute loss
        loss = yolo_loss(predictions, targets)
        
        # Backward pass and optimize
        loss.backward()
        optimizer.step()
    
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}')

import cv2
import torch

# Load the pre-trained YOLO model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)

# Initialize the video stream (replace '0' with a video file path for processing a video file)
cap = cv2.VideoCapture(0)

while True:
    # Read frames from the video stream
    ret, frame = cap.read()
    if not ret:
        break

    # Convert the frame to the format expected by the model
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = model([frame_rgb], size=640)  # Adjust size as needed

    # Render the detections on the frame
    frame_with_detections = results.render()[0]

    # Convert the frame back to BGR for displaying with OpenCV
    frame_with_detections_bgr = cv2.cvtColor(frame_with_detections, cv2.COLOR_RGB2BGR)

    # Display the frame with detections
    cv2.imshow('YOLO Object Detection', frame_with_detections_bgr)

    # Break the loop when 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video stream and close windows
cap.release()
cv2.destroyAllWindows()