关于#目标检测#的问题：小目标检测遇到瓶颈，我想问一下 在YOLOv5怎么用滑动窗口检测，有相关博文推荐吗(语言-python)

Question

小目标检测遇到瓶颈，总是有检错或者漏检，我想问一下 在YOLOv5怎么用滑动窗口检测，有相关博文推荐吗？

Answer 1

给你写个实例代码，希望采纳：

import torch
import cv2
from PIL import Image
from numpy import random
from matplotlib import pyplot as plt

# Load YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'custom', path_or_model='path/to/weights.pt')

# Define input image resolution and stride
img_size = 640  # input image size
stride = img_size // 2  # stride for sliding window

# Load input image
img_path = 'path/to/input/image.jpg'
img = Image.open(img_path)

# Convert image to numpy array
img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)

# Define sliding window parameters
window_size = (img_size, img_size)  # window size
overlap = 0.5  # overlap ratio

# Compute number of windows and positions
height, width, _ = img.shape
x_steps = int((width - window_size[0]) / (stride * overlap)) + 1
y_steps = int((height - window_size[1]) / (stride * overlap)) + 1

# Initialize empty detection list
detections = []

# Loop over windows
for i in range(x_steps):
    for j in range(y_steps):
        # Compute window position
        x = i * stride * overlap
        y = j * stride * overlap

        # Crop window from input image
        window = img[y:y+window_size[1], x:x+window_size[0]]

        # Convert window to PIL image
        window = Image.fromarray(window)

        # Run YOLOv5 model on window
        results = model(window)

        # Filter results by confidence threshold and class
        results.filter('class', 0, '>', 'confidence', 0.5)

        # Add window position to detection boxes
        for result in results.xyxy[0]:
            box = result.tolist()
            box[0] += x
            box[1] += y
            box[2] += x
            box[3] += y
            detections.append(box)

# Draw detection boxes on input image
for box in detections:
    x1, y1, x2, y2, conf, cls = box
    color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
    cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)

# Show output image
plt.imshow(img[:, :, ::-1])
plt.show()