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176 lines
7.3 KiB
Python
176 lines
7.3 KiB
Python
# Copyright (C) 2025 OpenMV, LLC.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in
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# the documentation and/or other materials provided with the
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# distribution.
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# 3. Any redistribution, use, or modification in source or binary form
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# is done solely for personal benefit and not for any commercial
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# purpose or for monetary gain. For commercial licensing options,
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# please contact openmv@openmv.io
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#
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# THIS SOFTWARE IS PROVIDED BY THE LICENSOR AND COPYRIGHT OWNER "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE LICENSOR OR COPYRIGHT
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# OWNER BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from ml.utils import *
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from micropython import const
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from ulab import numpy as np
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_NO_DETECTION = const(())
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class mediapipe_detection_postprocess:
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_MEDIAPIPE_CX = const(0)
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_MEDIAPIPE_CY = const(1)
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_MEDIAPIPE_CW = const(2)
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_MEDIAPIPE_CH = const(3)
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_MEDIAPIPE_KP = const(4)
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def __init__(self, threshold=0.6, anchors=None, anchor_grid=None, scores=[], cords=[],
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nms_threshold=0.1, nms_sigma=0.1):
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self.threshold = threshold
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self.anchors = anchors
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self.scores = scores
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self.cords = cords
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if self.anchors is None:
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anchor_count = sum((g * g) * d for g, d in anchor_grid)
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self.anchors = np.empty((anchor_count, 2))
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idx = 0
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for grid_size, scales in anchor_grid:
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for gy in range(grid_size):
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cy = (gy + 0.5) / grid_size
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for gx in range(grid_size):
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cx = (gx + 0.5) / grid_size
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for _ in range(scales):
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self.anchors[idx, 0] = cx
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self.anchors[idx, 1] = cy
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idx += 1
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self.nms_threshold = nms_threshold
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self.nms_sigma = nms_sigma
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def __call__(self, model, inputs, outputs):
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ib, ih, iw, ic = model.input_shape[0]
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nms = NMS(iw, ih, inputs[0].roi)
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output_len = outputs[0].shape[1]
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self.detection_post_process(ih, iw, nms, model, inputs, outputs, self.scores[0], self.cords[0],
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self.threshold, self.anchors[:output_len])
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if output_len < len(self.anchors):
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self.detection_post_process(ih, iw, nms, model, inputs, outputs, self.scores[1], self.cords[1],
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self.threshold, self.anchors[output_len:])
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return nms.get_bounding_boxes(threshold=self.nms_threshold, sigma=self.nms_sigma)[0]
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def detection_post_process(self, ih, iw, nms, model, inputs, outputs, score_idx, cords_idx, t, anchors):
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s_oh, s_ow, s_oc = model.output_shape[score_idx]
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scale = model.output_scale[score_idx]
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t = quantize(model, logit(t), index=score_idx)
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# Threshold all the scores
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score_row_outputs = outputs[score_idx].reshape((s_oh * s_ow * s_oc))
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score_indices = threshold(score_row_outputs, t, scale)
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if not len(score_indices):
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return _NO_DETECTION
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# Get the score information
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bb_scores = np.take(score_row_outputs, score_indices, axis=0)
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bb_scores = sigmoid(dequantize(model, bb_scores, index=score_idx))
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# Get the bounding boxes that have a valid score
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c_oh, c_ow, c_oc = model.output_shape[cords_idx]
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cords_row_outputs = outputs[cords_idx].reshape((c_oh * c_ow, c_oc))
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bb = dequantize(model, np.take(cords_row_outputs, score_indices, axis=0), index=cords_idx)
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# Get the anchor box information
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bb_a_array = np.take(anchors, score_indices, axis=0)
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# Compute the bounding box information
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ax = bb_a_array[:, _MEDIAPIPE_CX]
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ay = bb_a_array[:, _MEDIAPIPE_CY]
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x_center = bb[:, _MEDIAPIPE_CX] / iw + ax
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y_center = bb[:, _MEDIAPIPE_CY] / ih + ay
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w_rel = bb[:, _MEDIAPIPE_CW] / iw * 0.5
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h_rel = bb[:, _MEDIAPIPE_CH] / ih * 0.5
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# Get the keypoint information
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row_count = bb.shape[0]
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keypoints = np.empty((row_count, (c_oc - _MEDIAPIPE_KP) // 2, 2))
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keypoints[:, :, 0] = (bb[:, _MEDIAPIPE_KP::2] / iw + ax.reshape((row_count, 1))) * iw
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keypoints[:, :, 1] = (bb[:, _MEDIAPIPE_KP + 1::2] / ih + ay.reshape((row_count, 1))) * ih
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# Scale the bounding boxes to have enough integer precision for NMS
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xmin = (x_center - w_rel) * iw
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ymin = (y_center - h_rel) * ih
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xmax = (x_center + w_rel) * iw
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ymax = (y_center + h_rel) * ih
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for i in range(bb.shape[0]):
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nms.add_bounding_box(xmin[i], ymin[i], xmax[i], ymax[i], bb_scores[i], 0, keypoints=keypoints[i])
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class BlazeFace(mediapipe_detection_postprocess):
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def __init__(self, threshold=0.6, anchors=None, nms_threshold=0.1, nms_sigma=0.1):
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super().__init__(threshold=threshold, anchors=anchors,
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anchor_grid=[(16, 2), (8, 6)], scores=[1, 2], cords=[0, 3],
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nms_threshold=nms_threshold, nms_sigma=nms_sigma)
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class BlazePalm(mediapipe_detection_postprocess):
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def __init__(self, threshold=0.6, anchors=None, nms_threshold=0.1, nms_sigma=0.1):
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super().__init__(threshold=threshold, anchors=anchors,
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anchor_grid=[(24, 2), (12, 6)], scores=[0], cords=[1],
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nms_threshold=nms_threshold, nms_sigma=nms_sigma)
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class HandLandmarks:
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def __init__(self, threshold=0.6, nms_threshold=0.1, nms_sigma=0.1):
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self.threshold = threshold
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self.nms_threshold = nms_threshold
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self.nms_sigma = nms_sigma
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def __call__(self, model, inputs, outputs):
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ib, ih, iw, ic = model.input_shape[0]
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nms = NMS(iw, ih, inputs[0].roi)
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score = outputs[2][0, 0]
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if score < self.threshold:
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return _NO_DETECTION
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cords = outputs[3][0, :]
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# Get the keypoint information
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keypoints = np.empty((len(cords) // 3, 3))
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keypoints[:, 0] = cords[0::3]
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keypoints[:, 1] = cords[1::3]
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keypoints[:, 2] = cords[2::3]
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# Get bounding box information
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xmin = np.min(keypoints[:, 0])
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ymin = np.min(keypoints[:, 1])
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xmax = np.max(keypoints[:, 0])
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ymax = np.max(keypoints[:, 1])
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left_right = outputs[0][0, 0] > 0.5
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nms.add_bounding_box(xmin, ymin, xmax, ymax, score, left_right, keypoints=keypoints)
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return nms.get_bounding_boxes(threshold=self.nms_threshold, sigma=self.nms_sigma)
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