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我需要一些帮助,因为我将需要这个工作,我的最后论文项目的模型有2类inheat和非inheat,我做了下面的代码,但当它试图预测或检测的视频帧,它检测到非inheat,但它只堆叠在 *inheat帧,而不是 * non_inheat帧我可能做了一个错误的处理,在这里通过帧堆叠起来,即使它检测到非inheat,它堆叠到in_heat帧。
代码:
def process_video_with_second_model(video_path):
cap = cv2.VideoCapture(video_path)
class_counts = {'inheat': 0, 'non-inheat': 0}
in_heat_frames = []
non_in_heat_frames = []
while True:
ret, frame = cap.read()
if frame is None:
break # Break the loop when no more frames are available
# Resize the frame to a smaller size (e.g., 400x400)
frame_small = cv2.resize(frame, (400, 400))
# Use the second model to detect in-heat behavior
results_in_heat = yolov8_model_in_heat.predict(source=frame_small, show=True, conf=0.8)
# Print results to inspect structure
for results_in_heat_instance in results_in_heat:
# Access bounding box coordinates
boxes = results_in_heat_instance.boxes
# CONFIDENCE 0.5
if len(boxes) > 0:
class_name = results_in_heat_instance.names[0]
# Use a dictionary to store the counts for each class
class_counts[class_name] += 1
# Add the frame to the corresponding list based on the class name
if class_name == 'non-inheat':
non_in_heat_frames.append(frame)
elif class_name == 'inheat':
in_heat_frames.append(frame)
print(f"Class Counts: {class_counts}")
# Check if either condition is met (50 frames for inheat and 50 frames for non-inheat)
if class_counts['inheat'] >= 50 and class_counts['non-inheat'] >= 50:
break
# Release resources for the second model
cap.release()
cv2.destroyAllWindows()
# Stack the in-heat and non-in-heat frames vertically
stacked_in_heat_frames = np.vstack(in_heat_frames)
stacked_non_in_heat_frames = np.vstack(non_in_heat_frames)
# Display the stacked in-heat and non-in-heat frames
cv2.imshow('Stacked In-Heat Frames', stacked_in_heat_frames)
cv2.imshow('Stacked Non-In-Heat Frames', stacked_non_in_heat_frames)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Compare the counts and return the label with the higher count
if class_counts['inheat'] > class_counts['non-inheat']:
return 'inheat'
elif class_counts['non-inheat'] > class_counts['inheat']:
return 'non-inheat'
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我确实读了YOLOv8文档来预测,但还是做不到。
1条答案
按热度按时间1rhkuytd1#
问题出在这一行:
class_name = results_in_heat_instance.names[0]
,看结果的names对象:它是一个完整的模型名称字典,无论模型在一个帧中检测到什么,它都是一样的。要获得帧中每个检测到的对象的类名,您需要遍历boxes并获得每个box对象的cls值,这将是从上面提到的 *names字典 * 中检测到的类索引。示例如下所示:字符串