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274 lines
11 KiB
Python
274 lines
11 KiB
Python
# Copyright (C) 2024 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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import math
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import image
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from ml.utils import NMS
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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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# FOMO generates an image per class, where each pixel represents the centroid
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# of the trained object. These images are processed with `find_blobs()` to
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# extract centroids, and `get_stats()` is used to get their scores. Overlapping
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# detections are then filtered with NMS and positions are mapped back to the
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# original image, and a list of (rect, score) tuples is returned for each class,
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# representing detected objects.
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class fomo_postprocess:
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def __init__(self, threshold=0.4):
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self.threshold_list = [(math.ceil(threshold * 255), 255)]
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def __call__(self, model, inputs, outputs):
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n, oh, ow, oc = model.output_shape[0]
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nms = NMS(ow, oh, inputs[0].roi)
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for i in range(oc):
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img = image.Image(outputs[0][0, :, :, i] * 255)
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blobs = img.find_blobs(
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self.threshold_list, x_stride=1, area_threshold=1, pixels_threshold=1
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)
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for b in blobs:
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rect = b.rect()
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x, y, w, h = rect
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score = (
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img.get_statistics(thresholds=self.threshold_list, roi=rect).l_mean() / 255.0
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)
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nms.add_bounding_box(x, y, x + w, y + h, score, i)
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return nms.get_bounding_boxes()
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# This is a lightweight version of the tiny yolo v2 object detection algorithm.
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# It was optimized to work well on embedded devices with limited computational resources.
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class yolo_v2_postprocess:
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_YOLO_V2_TX = const(0)
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_YOLO_V2_TY = const(1)
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_YOLO_V2_TW = const(2)
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_YOLO_V2_TH = const(3)
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_YOLO_V2_SCORE = const(4)
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_YOLO_V2_CLASSES = const(5)
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def __init__(self, threshold=0.6, anchors=None, 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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if self.anchors is None:
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self.anchors = np.array([[0.98830, 3.36060],
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[2.11940, 5.37590],
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[3.05200, 9.13360],
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[5.55170, 9.30660],
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[9.72600, 11.1422]])
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self.anchors_len = len(self.anchors)
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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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ob, oh, ow, oc = model.output_shape[0]
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class_count = (oc // self.anchors_len) - _YOLO_V2_CLASSES
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-x))
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def mod(a, b):
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return a - (b * (a // b))
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def softmax(x):
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e_x = np.exp(x - np.max(x, axis=1, keepdims=True))
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return e_x / np.sum(e_x, axis=1, keepdims=True)
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# Reshape the output to a 2D array
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row_outputs = outputs[0].reshape((oh * ow * self.anchors_len,
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_YOLO_V2_CLASSES + class_count))
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# Threshold all the scores
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score_indices = sigmoid(row_outputs[:, _YOLO_V2_SCORE])
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score_indices = np.nonzero(score_indices > self.threshold)[0]
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if not len(score_indices):
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return _NO_DETECTION
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# Get the bounding boxes that have a valid score
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bb = np.take(row_outputs, score_indices, axis=0)
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# Extract rows, columns, and anchor indices
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bb_rows = score_indices // (ow * self.anchors_len)
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bb_cols = mod(score_indices // self.anchors_len, ow)
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bb_anchors = mod(score_indices, self.anchors_len)
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# Get the anchor box information
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bb_a_array = np.take(self.anchors, bb_anchors, axis=0)
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# Get the score information
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bb_scores = sigmoid(bb[:, _YOLO_V2_SCORE])
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# Get the class information
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bb_classes = np.argmax(softmax(bb[:, _YOLO_V2_CLASSES:]), axis=1)
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# Compute the bounding box information
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x_center = (bb_cols + sigmoid(bb[:, _YOLO_V2_TX])) / ow
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y_center = (bb_rows + sigmoid(bb[:, _YOLO_V2_TY])) / oh
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w_rel = (bb_a_array[:, 0] * np.exp(bb[:, _YOLO_V2_TW])) / ow
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h_rel = (bb_a_array[:, 1] * np.exp(bb[:, _YOLO_V2_TH])) / oh
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# Scale the bounding boxes to have enough integer precision for NMS
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ib, ih, iw, ic = model.input_shape[0]
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x_center = x_center * iw
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y_center = y_center * ih
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w_rel = w_rel * iw
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h_rel = h_rel * ih
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nms = NMS(iw, ih, inputs[0].roi)
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for i in range(bb.shape[0]):
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nms.add_bounding_box(x_center[i] - (w_rel[i] / 2),
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y_center[i] - (h_rel[i] / 2),
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x_center[i] + (w_rel[i] / 2),
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y_center[i] + (h_rel[i] / 2),
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bb_scores[i], bb_classes[i])
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return nms.get_bounding_boxes(threshold=self.nms_threshold, sigma=self.nms_sigma)
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# This is a lightweight version of the YOLO (You Only Look Once) object detection algorithm.
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# It is designed to work well on embedded devices with limited computational resources.
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class yolo_lc_postprocess(yolo_v2_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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if anchors is None:
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anchors = np.array([[0.076023, 0.258508],
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[0.163031, 0.413531],
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[0.234769, 0.702585],
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[0.427054, 0.715892],
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[0.748154, 0.857092]])
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super().__init__(threshold, anchors, nms_threshold, nms_sigma)
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class yolo_v5_postprocess:
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_YOLO_V5_CX = const(0)
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_YOLO_V5_CY = const(1)
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_YOLO_V5_CW = const(2)
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_YOLO_V5_CH = const(3)
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_YOLO_V5_SCORE = const(4)
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_YOLO_V5_CLASSES = const(5)
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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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oh, ow, oc = model.output_shape[0]
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class_count = oc - _YOLO_V5_CLASSES
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# Reshape the output to a 2D array
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row_outputs = outputs[0].reshape((oh * ow, _YOLO_V5_CLASSES + class_count))
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# Threshold all the scores
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score_indices = row_outputs[:, _YOLO_V5_SCORE]
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score_indices = np.nonzero(score_indices > self.threshold)[0]
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if not len(score_indices):
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return _NO_DETECTION
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# Get the bounding boxes that have a valid score
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bb = np.take(row_outputs, score_indices, axis=0)
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# Get the score information
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bb_scores = bb[:, _YOLO_V5_SCORE]
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# Get the class information
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bb_classes = np.argmax(bb[:, _YOLO_V5_CLASSES:], axis=1)
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# Compute the bounding box information
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x_center = bb[:, _YOLO_V5_CX]
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y_center = bb[:, _YOLO_V5_CY]
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w_rel = bb[:, _YOLO_V5_CW] * 0.5
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h_rel = bb[:, _YOLO_V5_CH] * 0.5
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# Scale the bounding boxes to have enough integer precision for NMS
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ib, ih, iw, ic = model.input_shape[0]
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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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nms = NMS(iw, ih, inputs[0].roi)
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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],
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bb_scores[i], bb_classes[i])
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return nms.get_bounding_boxes(threshold=self.nms_threshold, sigma=self.nms_sigma)
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class yolo_v8_postprocess:
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_YOLO_V8_CX = const(0)
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_YOLO_V8_CY = const(1)
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_YOLO_V8_CW = const(2)
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_YOLO_V8_CH = const(3)
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_YOLO_V8_CLASSES = const(4)
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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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oh, ow, oc = model.output_shape[0]
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class_count = ow - _YOLO_V8_CLASSES
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# Reshape the output to a 2D array
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column_outputs = outputs[0].reshape((oh * (_YOLO_V8_CLASSES + class_count), oc))
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# Threshold all the scores
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score_indices = np.max(column_outputs[_YOLO_V8_CLASSES:, :], axis=0)
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score_indices = np.nonzero(score_indices > self.threshold)[0]
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if not len(score_indices):
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return _NO_DETECTION
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# Get the bounding boxes that have a valid score
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bb = np.take(column_outputs, score_indices, axis=1)
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# Get the score information
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bb_scores = np.max(bb[_YOLO_V8_CLASSES:, :], axis=0)
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# Get the class information
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bb_classes = np.argmax(bb[_YOLO_V8_CLASSES:, :], axis=0)
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# Compute the bounding box information
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x_center = bb[_YOLO_V8_CX, :]
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y_center = bb[_YOLO_V8_CY, :]
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w_rel = bb[_YOLO_V8_CW, :] * 0.5
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h_rel = bb[_YOLO_V8_CH, :] * 0.5
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# Scale the bounding boxes to have enough integer precision for NMS
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ib, ih, iw, ic = model.input_shape[0]
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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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nms = NMS(iw, ih, inputs[0].roi)
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for i in range(bb.shape[1]):
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nms.add_bounding_box(xmin[i], ymin[i], xmax[i], ymax[i],
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bb_scores[i], bb_classes[i])
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return nms.get_bounding_boxes(threshold=self.nms_threshold, sigma=self.nms_sigma)
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