65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
import fiftyone as fo
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from PIL import Image
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from detection import detect
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name = "dataset-small"
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dataset_dir = "/home/zenon/Documents/master-thesis/classification/evaluation/dataset-small"
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# The splits to load
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splits = ["val"]
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# Load the dataset, using tags to mark the samples in each split
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dataset = fo.Dataset(name)
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for split in splits:
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dataset.add_dir(
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dataset_dir=dataset_dir,
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dataset_type=fo.types.YOLOv5Dataset,
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split=split,
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tags=split,
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)
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classes = dataset.default_classes
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predictions_view = dataset.view()
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with fo.ProgressBar() as pb:
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for sample in pb(predictions_view):
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image = Image.open(sample.filepath)
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w, h = image.size
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pred = detect(sample.filepath, '../weights/yolo.onnx', '../weights/resnet.onnx')
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detections = []
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for _, row in pred.iterrows():
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xmin, xmax = int(row['xmin']), int(row['xmax'])
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ymin, ymax = int(row['ymin']), int(row['ymax'])
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rel_box = [
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xmin / w, ymin / h, (xmax - xmin) / w, (ymax - ymin) / h
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]
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detections.append(
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fo.Detection(label=classes[int(row['cls'])],
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bounding_box=rel_box,
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confidence=int(row['cls_conf'])))
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sample["yolo_resnet"] = fo.Detections(detections=detections)
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sample.save()
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results = predictions_view.evaluate_detections(
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"yolo_resnet",
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gt_field="ground_truth",
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eval_key="eval",
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compute_mAP=True,
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)
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# Get the 10 most common classes in the dataset
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counts = dataset.count_values("ground_truth.detections.label")
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classes_top10 = sorted(counts, key=counts.get, reverse=True)[:10]
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# Print a classification report for the top-10 classes
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results.print_report(classes=classes_top10)
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plot = results.plot_pr_curves(classes=["Healthy", "Stressed"])
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plot.show()
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session = fo.launch_app(dataset)
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session.view = predictions_view
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session.wait()
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