52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import os
|
|
import pandas as pd
|
|
from ultralytics import YOLO
|
|
import cv2
|
|
|
|
video_path = r'D:\AIM\lemon\test.mp4'
|
|
video_path_out = r'D:\AIM\lemon\test_out.mp4'
|
|
|
|
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', 'train', 'weights', 'last.pt')
|
|
|
|
# Load a model
|
|
model = YOLO(r"D:\AIM\lemon\YOLO-Training\YOLOv11_finetune\weights\best.pt") # load a custom model
|
|
|
|
threshold = 0.5
|
|
classifications = []
|
|
|
|
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)
|
|
|
|
# percentage =
|
|
|
|
# if results.names[int(class_id)] in classifications:
|
|
|
|
# else:
|
|
classifications.append(results.names[int(class_id)])
|
|
|
|
out.write(frame)
|
|
ret, frame = cap.read()
|
|
|
|
# Create a new DataFrame from the classifications
|
|
new_df = pd.DataFrame(classifications)
|
|
|
|
# Write the new DataFrame to an Excel file
|
|
new_df.to_excel('output_classifications.xlsx', index=False)
|
|
|
|
cap.release()
|
|
out.release()
|
|
cv2.destroyAllWindows() |