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predict_video.py
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predict_video.py
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import os
from ultralytics import YOLO
import cv2
VIDEOS_DIR = os.path.join('.', 'videos')
video_path = os.path.join(VIDEOS_DIR, 'alpaca3.mp4')
video_path_out = '{}_out.mp4'.format(video_path)
cap = cv2.VideoCapture(video_path)
ret, frame = cap.read()
H, W, _ = frame.shape
out = cv2.VideoWriter(video_path_out, cv2.VideoWriter_fourcc(*'MP4V'), int(cap.get(cv2.CAP_PROP_FPS)), (W, H))
model_path = os.path.join('.', 'runs', 'detect', 'train2', 'weights', 'last.pt')
# Load a model
model = YOLO(model_path) # load a custom model
threshold = 0.5
while ret:
results = model(frame)[0]
for result in results.boxes.data.tolist():
x1, y1, x2, y2, score, class_id = result
if score > threshold:
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 4)
cv2.putText(frame, results.names[int(class_id)].upper(), (int(x1), int(y1 - 10)),
cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 255, 0), 3, cv2.LINE_AA)
out.write(frame)
ret, frame = cap.read()
cap.release()
out.release()
cv2.destroyAllWindows()