Closed Joeyabuki99 closed 3 months ago
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Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our YOLOv8 Docs for details and get started with:
pip install ultralytics
Hi there! Thanks for reaching out with your question. It looks like you're trying to combine a YOLOv8 detector with an EfficientNetB3 classifier, which is a great approach for leveraging the strengths of both models. Let's address the issue you're encountering.
The error TypeError: 'dict' object is not callable
suggests that the detector
object might not be correctly instantiated or used. YOLOv8 models typically return a dictionary-like object, and you might need to access specific attributes or methods to get the results.
Here's a revised version of your code with some adjustments:
torch
and yolov5
(or yolov8
if applicable).import torch
from tensorflow.keras.models import load_model
import cv2
# Load YOLOv8 model
detector = torch.load('/myPath/yolov8m_trained.pt')
# Load EfficientNetB3 classifier
classifier = load_model('/myPath/efficientnet_model_unfreeze.h5')
video_path = '/myPath/video_test.mp4'
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Perform detection
results = detector(frame)
detections = results['pred'][0].cpu().numpy() # Adjust based on the actual output structure
for detection in detections:
x1, y1, x2, y2, conf, cls = detection
roi = frame[int(y1):int(y2), int(x1):int(x2)]
roi_resized = cv2.resize(roi, (224, 224))
roi_resized = roi_resized / 255.0
roi_resized = roi_resized.reshape(1, 224, 224, 3)
pred = classifier.predict(roi_resized)
class_id = pred.argmax(axis=1)[0]
label = f'Class: {class_id}, Conf: {conf:.2f}'
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
cv2.putText(frame, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
output_path = 'output_video.mp4'
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, 30.0, (int(cap.get(3)), int(cap.get(4))))
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
results = detector(frame)
detections = results['pred'][0].cpu().numpy() # Adjust based on the actual output structure
for detection in detections:
x1, y1, x2, y2, conf, cls = detection
roi = frame[int(y1):int(y2), int(x1):int(x2)]
roi_resized = cv2.resize(roi, (224, 224))
roi_resized = roi_resized / 255.0
roi_resized = roi_resized.reshape(1, 224, 224, 3)
pred = classifier.predict(roi_resized)
class_id = pred.argmax(axis=1)[0]
label = f'Class: {class_id}, Conf: {conf:.2f}'
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
cv2.putText(frame, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
out.write(frame)
cap.release()
out.release()
cv2.destroyAllWindows()
Please ensure you have the latest versions of torch
and yolov5
(or yolov8
if applicable). If the issue persists, providing a minimum reproducible example would help us investigate further. You can find more details on creating a reproducible example here.
Feel free to reach out if you have any more questions or need further assistance. Happy coding! 🚀
Hi @glenn-jocher , thank you. I noticed that this problem can be resolved loading the yolo model with detector = YOLO('/myPath/best.pt')
.
Is this ok or should I load it with the torch model load
?
Hi @Joeyabuki99,
Thank you for your question! It's great to hear that you've found a way to resolve the issue by loading the YOLO model with detector = YOLO('/myPath/best.pt')
. This approach is indeed correct and recommended for using YOLO models, as it ensures that all the necessary configurations and dependencies are properly handled.
Using YOLO('/myPath/best.pt')
is the preferred method because it leverages the YOLO class's built-in functionalities, which are optimized for loading and running inference with YOLO models. This method abstracts away some of the complexities and potential pitfalls associated with directly using torch.load
.
Here's a quick example to illustrate the correct usage:
from yolov5 import YOLO
from tensorflow.keras.models import load_model
import cv2
# Load YOLOv5 model
detector = YOLO('/myPath/best.pt')
# Load EfficientNetB3 classifier
classifier = load_model('/myPath/efficientnet_model_unfreeze.h5')
video_path = '/myPath/video_test.mp4'
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Perform detection
results = detector(frame)
detections = results.xyxy[0].cpu().numpy() # Adjust based on the actual output structure
for detection in detections:
x1, y1, x2, y2, conf, cls = detection
roi = frame[int(y1):int(y2), int(x1):int(x2)]
roi_resized = cv2.resize(roi, (224, 224))
roi_resized = roi_resized / 255.0
roi_resized = roi_resized.reshape(1, 224, 224, 3)
pred = classifier.predict(roi_resized)
class_id = pred.argmax(axis=1)[0]
label = f'Class: {class_id}, Conf: {conf:.2f}'
cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
cv2.putText(frame, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
This should work seamlessly for your use case. If you encounter any further issues or have additional questions, please don't hesitate to ask. The YOLO community and the Ultralytics team are here to help! 😊
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Question
Hi guys, I want to combine togheter a detector trained with YOLOv8 and a classifier done with EfficientNetB3. My detector is been saved with the
model.save(output_model_path)
and so has a.pt
extension, while the classifier is been saved with the same method but has a.h5
extension.Now how can I combine these two models and test the system on a custom video? I have tried this code but I'm receiving the error
TypeError: 'dict' object is not callable
on theresults = detector(frame)
(line 20). Thanks` import torch from tensorflow.keras.models import load_model import cv2