mirror of
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44 lines
2.0 KiB
Python
44 lines
2.0 KiB
Python
import logging
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import numpy as np
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import cv2
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logger = logging.getLogger(__name__)
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def yolov8_preprocess(tensor_input, model_input_shape):
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# tensor_input must be nhwc
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assert tensor_input.shape[3] == 3
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if tuple(tensor_input.shape[1:3]) != tuple(model_input_shape[2:4]):
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logger.warn(f"yolov8_preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!")
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# cv2.dnn.blobFromImage is faster than numpying it
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return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)
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def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20, score_threshold = 0.3, nms_threshold = 0.5):
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model_box_count = tensor_output.shape[2]
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probs = tensor_output[0, 4:, :]
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all_ids = np.argmax(probs, axis=0)
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all_confidences = probs.T[np.arange(model_box_count), all_ids]
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all_boxes = tensor_output[0, 0:4, :].T
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mask = (all_confidences > score_threshold)
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class_ids = all_ids[mask]
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confidences = all_confidences[mask]
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cx, cy, w, h = all_boxes[mask].T
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if model_input_shape[3] == 3:
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scale_y, scale_x = 1 / model_input_shape[1], 1 / model_input_shape[2]
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else:
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scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3]
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detections = np.stack((class_ids, confidences, scale_y * (cy - h / 2), scale_x * (cx - w / 2), scale_y * (cy + h / 2), scale_x * (cx + w / 2)), axis=1)
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if detections.shape[0] > box_count:
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# if too many detections, do nms filtering to suppress overlapping boxes
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boxes = np.stack((cx - w / 2, cy - h / 2, w, h), axis=1)
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indexes = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold, nms_threshold)
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detections = detections[indexes]
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# if still too many, trim the rest by confidence
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if detections.shape[0] > box_count:
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detections = detections[np.argpartition(detections[:,1], -box_count)[-box_count:]]
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detections = detections.copy()
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detections.resize((box_count, 6))
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return detections
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