Adding predictions to a `fiftyone` dataset.

fiftyone_predictions.py

# Add predictions to samples
with fo.ProgressBar() as pb:
    for sample in pb(dataset.view()):
        # Load image

        image = cv2.imread(sample.filepath)
        h, w, c = image.shape
        
        # Perform inference
        results   = model_best(image, size=3050, augment=False)
        preds   = results.pandas().xyxy[0]
        boxes  = preds[['xmin','ymin','xmax','ymax']].values
        scores   = preds.confidence.values
        labels = len(boxes) * ["cots"]

        # Convert detections to FiftyOne format
        detections = []
        for label, score, box in zip(labels, scores, boxes):
            # Convert to [top-left-x, top-left-y, width, height]
            # in relative coordinates in [0, 1] x [0, 1]
            # box = [box[0], box[1], box[0]+box[2], box[1]+box[3]]
            x1, y1, x2, y2 = box
            rel_box = [x1 / w, y1 / h, (x2 - x1) / w, (y2 - y1) / h]
            detections.append(fo.Detection(
                label=label,
                bounding_box=rel_box,
                confidence=score
            ))
        
        # Save predictions to dataset
        sample["predictions"]= fo.Detections(detections=detections)
        sample.save()