True Positive detections

[ilpapds]

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Question

Hello, i am trying to store/save the true positive (correct) detected bounding boxes of the yolov5 model. In order to do that, i am trying to modify the val.py code in the following lines:

        # Evaluate
        if nl:
            tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
            scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
            labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
            correct = process_batch(predn, labelsn, iouv)
            if plots:
                confusion_matrix.process_batch(predn, labelsn)
        stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0]))  # (correct, conf, pcls, tcls)

Modyfying them in this way:

        # Evaluate
        if nl:
            tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
            scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
            labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
            correct = process_batch(predn, labelsn, iouv)
            for batch_j in correct[batch_j, 0]:  
                init_img = cv2.imread(paths[batch_j])                               
                crop_img = init_img[int(predn[batch_j,0]):int(predn[batch_j,2]),int(predn[batch_j,1]):int(predn[batch_j,3]),:]     
            if plots:
                confusion_matrix.process_batch(predn, labelsn)
        stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0]))  # (correct, conf, pcls, tcls)

And now i want to store this crop_img which corresponds to the true positive detections. Is that possible? Has anybody done it with another way? Thank you mery much

Additional

No response

[github-actions]

👋 Hello @ilpapds, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@ilpapds 👋 Hello! Thanks for asking about cropping results with YOLOv5 🚀. Cropping bounding box detections can be useful for training classification models on box contents for example. This feature was added in PR #2827. You can crop detections using either detect.py or YOLOv5 PyTorch Hub:

detect.py

Crops will be saved under runs/detect/exp/crops, with a directory for each class detected.

python detect.py --save-crop

YOLOv5 PyTorch Hub

Crops will be saved under runs/detect/exp/crops if save=True, and also returned as a dictionary with crops as numpy arrays.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
crops = results.crop(save=True)  # or .show(), .save(), .print(), .pandas(), etc.

Good luck 🍀 and let us know if you have any other questions!

[ilpapds]

@glenn-jocher thank you for your quick response. However, instead of cropping the detected bounding boxes, i wish to filter only the true positive ones. This is why i am trying to use the val.py code. Could you please help me on how to achieve this? Thank you

[glenn-jocher]

@ilpapds sorry, we don't have resources to review or assist with custom code, but perhaps you can use the above examples to help build your custom solution.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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