YOLOv5 ist eine Implementierung des [[https://arxiv.org/abs/1506.02640|YOLO Paper]] (You only look once) zum Thema Object Detection von Ultralytics. Die aktuelle Version ist [[https://github.com/ultralytics/ultralytics|YOLOv8]].

=====Image Detection=====
<code python>
# pip install torch torchvision pandas ultralytics matplotlib

import torch
import matplotlib
import matplotlib.pyplot as plt
import sys

model = torch.hub.load('ultralytics/yolov5', 'yolov5x')
img = sys.argv[1]
results = model(img)

threshold = 0.6

filtered_results = []
for det in results.pred[0]:  
    if det[4] >= threshold:   
        filtered_results.append(det)

for det in filtered_results:
    x1, y1, x2, y2, prob, cls = det[:6]
    class_names = model.model.names
    class_name = class_names[int(cls)]
    print(f"{class_name} with {prob:.4f} probability at ({x1:.2f}, {y1:.2f}) - ({x2:.2f}, {y2:.2f})")


if filtered_results:
    results.pred[0] = torch.stack(filtered_results)
    results.show()  # results.save(save_dir='results') 
</code>

=====Video Detection=====

<code python>
# pip install torch torchvision pandas ultralytics matplotlib

import torch
import matplotlib.pyplot as plt
import cv2
import sys

# Load YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'yolov5x')

# Open video capture
video_path = sys.argv[1]
cap = cv2.VideoCapture(video_path)

target_frame = 4000  # Replace with the desired frame index to forward to
cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame)

threshold = 0.001

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    
    # Perform object detection on the current frame
    results = model(frame)
    filtered_results = []
    
    for det in results.pred[0]:
        if det[4] >= threshold:
            filtered_results.append(det)
    
    if filtered_results:
        results.pred[0] = torch.stack(filtered_results)
        frame_with_results = results.render()[0]
        
        # Display the frame with detected objects
        cv2.imshow('Object Detection', frame_with_results)
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
</code>

=====Links=====

  * [[https://towardsdatascience.com/the-practical-guide-for-object-detection-with-yolov5-algorithm-74c04aac4843|The practical guide for Object Detection with YOLOv5]]
  * [[https://www.youtube.com/watch?v=wM1wn1bZ3S4|Prepare a custom dataset]]
  * [[https://colab.research.google.com/drive/1nKoC-_areXmc_20Xn7z6kcqHEKU7SJsX|How to use Yolov7 - Colab]]