【光电智造】纵观全局：YOLO助力实时物体检测原理及代码

import torchimport torchvision.transforms as transformsfrom PIL import Image
# Load a pre-trained YOLO modelmodel = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
# Function to perform object detectiondef detect_objects(image_path):# Load and transform the imageimage = Image.open(image_path)transform = transforms.Compose([transforms.ToTensor(),])image = transform(image).unsqueeze(0) # Add batch dimension
# Perform inferencemodel.eval() # Set the model to evaluation modewith torch.no_grad():predictions = model(image)
# Process predictions# Note: The output format can vary, so adjust the processing as neededfor pred in predictions[0]:bbox = pred[:4] # Bounding box coordinatesscore = pred[4] # Confidence scoreclass_id = pred[5] # Class IDprint(f'Class: {class_id}, Score: {score}, BBox: {bbox}')
# Example usagedetect_objects('path/to/your/image.jpg')

import torchimport torch.optim as optim
# Assuming yolo_model is your YOLO model and train_loader is your data loader
# Define the optimizeroptimizer = 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 formatdef 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 loopfor 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 cv2import torch
# Load the pre-trained YOLO modelmodel = 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 windowscap.release()cv2.destroyAllWindows()