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d789cb459c
dnn: cleanup of halide backend for 5.x #24231 Merge with https://github.com/opencv/opencv_extra/pull/1092. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
404 lines
16 KiB
Python
404 lines
16 KiB
Python
import argparse
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import cv2 as cv
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import numpy as np
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import os
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"""
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Link to original paper : https://arxiv.org/abs/1812.11703
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Link to original repo : https://github.com/STVIR/pysot
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You can download the pre-trained weights of the Tracker Model from https://drive.google.com/file/d/11bwgPFVkps9AH2NOD1zBDdpF_tQghAB-/view?usp=sharing
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You can download the target net (target branch of SiamRPN++) from https://drive.google.com/file/d/1dw_Ne3UMcCnFsaD6xkZepwE4GEpqq7U_/view?usp=sharing
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You can download the search net (search branch of SiamRPN++) from https://drive.google.com/file/d/1Lt4oE43ZSucJvze3Y-Z87CVDreO-Afwl/view?usp=sharing
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You can download the head model (RPN Head) from https://drive.google.com/file/d/1zT1yu12mtj3JQEkkfKFJWiZ71fJ-dQTi/view?usp=sharing
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"""
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class ModelBuilder():
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""" This class generates the SiamRPN++ Tracker Model by using Imported ONNX Nets
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"""
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def __init__(self, target_net, search_net, rpn_head):
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super(ModelBuilder, self).__init__()
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# Build the target branch
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self.target_net = target_net
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# Build the search branch
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self.search_net = search_net
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# Build RPN_Head
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self.rpn_head = rpn_head
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def template(self, z):
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""" Takes the template of size (1, 1, 127, 127) as an input to generate kernel
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"""
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self.target_net.setInput(z)
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outNames = self.target_net.getUnconnectedOutLayersNames()
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self.zfs_1, self.zfs_2, self.zfs_3 = self.target_net.forward(outNames)
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def track(self, x):
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""" Takes the search of size (1, 1, 255, 255) as an input to generate classification score and bounding box regression
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"""
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self.search_net.setInput(x)
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outNames = self.search_net.getUnconnectedOutLayersNames()
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xfs_1, xfs_2, xfs_3 = self.search_net.forward(outNames)
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self.rpn_head.setInput(np.stack([self.zfs_1, self.zfs_2, self.zfs_3]), 'input_1')
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self.rpn_head.setInput(np.stack([xfs_1, xfs_2, xfs_3]), 'input_2')
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outNames = self.rpn_head.getUnconnectedOutLayersNames()
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cls, loc = self.rpn_head.forward(outNames)
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return {'cls': cls, 'loc': loc}
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class Anchors:
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""" This class generate anchors.
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"""
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def __init__(self, stride, ratios, scales, image_center=0, size=0):
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self.stride = stride
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self.ratios = ratios
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self.scales = scales
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self.image_center = image_center
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self.size = size
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self.anchor_num = len(self.scales) * len(self.ratios)
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self.anchors = self.generate_anchors()
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def generate_anchors(self):
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"""
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generate anchors based on predefined configuration
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"""
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anchors = np.zeros((self.anchor_num, 4), dtype=np.float32)
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size = self.stride**2
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count = 0
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for r in self.ratios:
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ws = int(np.sqrt(size * 1. / r))
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hs = int(ws * r)
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for s in self.scales:
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w = ws * s
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h = hs * s
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anchors[count][:] = [-w * 0.5, -h * 0.5, w * 0.5, h * 0.5][:]
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count += 1
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return anchors
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class SiamRPNTracker:
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def __init__(self, model):
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super(SiamRPNTracker, self).__init__()
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self.anchor_stride = 8
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self.anchor_ratios = [0.33, 0.5, 1, 2, 3]
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self.anchor_scales = [8]
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self.track_base_size = 8
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self.track_context_amount = 0.5
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self.track_exemplar_size = 127
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self.track_instance_size = 255
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self.track_lr = 0.4
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self.track_penalty_k = 0.04
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self.track_window_influence = 0.44
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self.score_size = (self.track_instance_size - self.track_exemplar_size) // \
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self.anchor_stride + 1 + self.track_base_size
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self.anchor_num = len(self.anchor_ratios) * len(self.anchor_scales)
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hanning = np.hanning(self.score_size)
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window = np.outer(hanning, hanning)
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self.window = np.tile(window.flatten(), self.anchor_num)
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self.anchors = self.generate_anchor(self.score_size)
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self.model = model
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def get_subwindow(self, im, pos, model_sz, original_sz, avg_chans):
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"""
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Args:
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im: bgr based input image frame
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pos: position of the center of the frame
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model_sz: exemplar / target image size
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s_z: original / search image size
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avg_chans: channel average
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Return:
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im_patch: sub_windows for the given image input
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"""
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if isinstance(pos, float):
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pos = [pos, pos]
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sz = original_sz
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im_h, im_w, im_d = im.shape
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c = (original_sz + 1) / 2
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cx, cy = pos
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context_xmin = np.floor(cx - c + 0.5)
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context_xmax = context_xmin + sz - 1
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context_ymin = np.floor(cy - c + 0.5)
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context_ymax = context_ymin + sz - 1
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left_pad = int(max(0., -context_xmin))
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top_pad = int(max(0., -context_ymin))
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right_pad = int(max(0., context_xmax - im_w + 1))
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bottom_pad = int(max(0., context_ymax - im_h + 1))
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context_xmin += left_pad
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context_xmax += left_pad
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context_ymin += top_pad
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context_ymax += top_pad
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if any([top_pad, bottom_pad, left_pad, right_pad]):
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size = (im_h + top_pad + bottom_pad, im_w + left_pad + right_pad, im_d)
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te_im = np.zeros(size, np.uint8)
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te_im[top_pad:top_pad + im_h, left_pad:left_pad + im_w, :] = im
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if top_pad:
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te_im[0:top_pad, left_pad:left_pad + im_w, :] = avg_chans
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if bottom_pad:
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te_im[im_h + top_pad:, left_pad:left_pad + im_w, :] = avg_chans
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if left_pad:
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te_im[:, 0:left_pad, :] = avg_chans
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if right_pad:
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te_im[:, im_w + left_pad:, :] = avg_chans
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im_patch = te_im[int(context_ymin):int(context_ymax + 1),
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int(context_xmin):int(context_xmax + 1), :]
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else:
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im_patch = im[int(context_ymin):int(context_ymax + 1),
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int(context_xmin):int(context_xmax + 1), :]
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if not np.array_equal(model_sz, original_sz):
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im_patch = cv.resize(im_patch, (model_sz, model_sz))
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im_patch = im_patch.transpose(2, 0, 1)
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im_patch = im_patch[np.newaxis, :, :, :]
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im_patch = im_patch.astype(np.float32)
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return im_patch
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def generate_anchor(self, score_size):
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"""
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Args:
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im: bgr based input image frame
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pos: position of the center of the frame
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model_sz: exemplar / target image size
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s_z: original / search image size
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avg_chans: channel average
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Return:
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anchor: anchors for pre-determined values of stride, ratio, and scale
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"""
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anchors = Anchors(self.anchor_stride, self.anchor_ratios, self.anchor_scales)
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anchor = anchors.anchors
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x1, y1, x2, y2 = anchor[:, 0], anchor[:, 1], anchor[:, 2], anchor[:, 3]
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anchor = np.stack([(x1 + x2) * 0.5, (y1 + y2) * 0.5, x2 - x1, y2 - y1], 1)
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total_stride = anchors.stride
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anchor_num = anchors.anchor_num
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anchor = np.tile(anchor, score_size * score_size).reshape((-1, 4))
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ori = - (score_size // 2) * total_stride
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xx, yy = np.meshgrid([ori + total_stride * dx for dx in range(score_size)],
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[ori + total_stride * dy for dy in range(score_size)])
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xx, yy = np.tile(xx.flatten(), (anchor_num, 1)).flatten(), \
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np.tile(yy.flatten(), (anchor_num, 1)).flatten()
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anchor[:, 0], anchor[:, 1] = xx.astype(np.float32), yy.astype(np.float32)
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return anchor
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def _convert_bbox(self, delta, anchor):
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"""
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Args:
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delta: localisation
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anchor: anchor of pre-determined anchor size
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Return:
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delta: prediction of bounding box
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"""
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delta_transpose = np.transpose(delta, (1, 2, 3, 0))
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delta_contig = np.ascontiguousarray(delta_transpose)
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delta = delta_contig.reshape(4, -1)
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delta[0, :] = delta[0, :] * anchor[:, 2] + anchor[:, 0]
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delta[1, :] = delta[1, :] * anchor[:, 3] + anchor[:, 1]
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delta[2, :] = np.exp(delta[2, :]) * anchor[:, 2]
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delta[3, :] = np.exp(delta[3, :]) * anchor[:, 3]
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return delta
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def _softmax(self, x):
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"""
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Softmax in the direction of the depth of the layer
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"""
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x = x.astype(dtype=np.float32)
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x_max = x.max(axis=1)[:, np.newaxis]
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e_x = np.exp(x-x_max)
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div = np.sum(e_x, axis=1)[:, np.newaxis]
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y = e_x / div
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return y
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def _convert_score(self, score):
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"""
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Args:
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cls: score
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Return:
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cls: score for cls
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"""
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score_transpose = np.transpose(score, (1, 2, 3, 0))
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score_con = np.ascontiguousarray(score_transpose)
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score_view = score_con.reshape(2, -1)
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score = np.transpose(score_view, (1, 0))
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score = self._softmax(score)
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return score[:,1]
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def _bbox_clip(self, cx, cy, width, height, boundary):
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"""
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Adjusting the bounding box
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"""
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bbox_h, bbox_w = boundary
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cx = max(0, min(cx, bbox_w))
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cy = max(0, min(cy, bbox_h))
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width = max(10, min(width, bbox_w))
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height = max(10, min(height, bbox_h))
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return cx, cy, width, height
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def init(self, img, bbox):
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"""
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Args:
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img(np.ndarray): bgr based input image frame
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bbox: (x, y, w, h): bounding box
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"""
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x, y, w, h = bbox
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self.center_pos = np.array([x + (w - 1) / 2, y + (h - 1) / 2])
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self.h = h
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self.w = w
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w_z = self.w + self.track_context_amount * np.add(h, w)
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h_z = self.h + self.track_context_amount * np.add(h, w)
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s_z = round(np.sqrt(w_z * h_z))
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self.channel_average = np.mean(img, axis=(0, 1))
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z_crop = self.get_subwindow(img, self.center_pos, self.track_exemplar_size, s_z, self.channel_average)
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self.model.template(z_crop)
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def track(self, img):
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"""
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Args:
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img(np.ndarray): BGR image
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Return:
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bbox(list):[x, y, width, height]
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"""
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w_z = self.w + self.track_context_amount * np.add(self.w, self.h)
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h_z = self.h + self.track_context_amount * np.add(self.w, self.h)
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s_z = np.sqrt(w_z * h_z)
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scale_z = self.track_exemplar_size / s_z
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s_x = s_z * (self.track_instance_size / self.track_exemplar_size)
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x_crop = self.get_subwindow(img, self.center_pos, self.track_instance_size, round(s_x), self.channel_average)
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outputs = self.model.track(x_crop)
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score = self._convert_score(outputs['cls'])
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pred_bbox = self._convert_bbox(outputs['loc'], self.anchors)
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def change(r):
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return np.maximum(r, 1. / r)
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def sz(w, h):
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pad = (w + h) * 0.5
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return np.sqrt((w + pad) * (h + pad))
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# scale penalty
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s_c = change(sz(pred_bbox[2, :], pred_bbox[3, :]) /
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(sz(self.w * scale_z, self.h * scale_z)))
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# aspect ratio penalty
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r_c = change((self.w / self.h) /
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(pred_bbox[2, :] / pred_bbox[3, :]))
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penalty = np.exp(-(r_c * s_c - 1) * self.track_penalty_k)
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pscore = penalty * score
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# window penalty
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pscore = pscore * (1 - self.track_window_influence) + \
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self.window * self.track_window_influence
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best_idx = np.argmax(pscore)
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bbox = pred_bbox[:, best_idx] / scale_z
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lr = penalty[best_idx] * score[best_idx] * self.track_lr
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cpx, cpy = self.center_pos
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x,y,w,h = bbox
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cx = x + cpx
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cy = y + cpy
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# smooth bbox
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width = self.w * (1 - lr) + w * lr
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height = self.h * (1 - lr) + h * lr
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# clip boundary
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cx, cy, width, height = self._bbox_clip(cx, cy, width, height, img.shape[:2])
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# update state
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self.center_pos = np.array([cx, cy])
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self.w = width
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self.h = height
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bbox = [cx - width / 2, cy - height / 2, width, height]
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best_score = score[best_idx]
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return {'bbox': bbox, 'best_score': best_score}
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def get_frames(video_name):
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"""
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Args:
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Path to input video frame
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Return:
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Frame
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"""
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cap = cv.VideoCapture(video_name if video_name else 0)
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while True:
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ret, frame = cap.read()
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if ret:
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yield frame
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else:
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break
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def main():
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""" Sample SiamRPN Tracker
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"""
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# Computation backends supported by layers
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backends = (cv.dnn.DNN_BACKEND_DEFAULT, cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_BACKEND_OPENCV,
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cv.dnn.DNN_BACKEND_VKCOM, cv.dnn.DNN_BACKEND_CUDA)
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# Target Devices for computation
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targets = (cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_OPENCL, cv.dnn.DNN_TARGET_OPENCL_FP16, cv.dnn.DNN_TARGET_MYRIAD,
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cv.dnn.DNN_TARGET_VULKAN, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16)
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parser = argparse.ArgumentParser(description='Use this script to run SiamRPN++ Visual Tracker',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--input_video', type=str, help='Path to input video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--target_net', type=str, default='target_net.onnx', help='Path to part of SiamRPN++ ran on target frame.')
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parser.add_argument('--search_net', type=str, default='search_net.onnx', help='Path to part of SiamRPN++ ran on search frame.')
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parser.add_argument('--rpn_head', type=str, default='rpn_head.onnx', help='Path to RPN Head ONNX model.')
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parser.add_argument('--backend', choices=backends, default=cv.dnn.DNN_BACKEND_DEFAULT, type=int,
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help="Select a computation backend: "
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"%d: automatically (by default), "
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"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"%d: OpenCV Implementation, "
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"%d: VKCOM, "
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"%d: CUDA" % backends)
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parser.add_argument('--target', choices=targets, default=cv.dnn.DNN_TARGET_CPU, type=int,
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help='Select a target device: '
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'%d: CPU target (by default), '
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'%d: OpenCL, '
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'%d: OpenCL FP16, '
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'%d: Myriad, '
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'%d: Vulkan, '
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'%d: CUDA, '
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'%d: CUDA fp16 (half-float preprocess)' % targets)
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args, _ = parser.parse_known_args()
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if args.input_video and not os.path.isfile(args.input_video):
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raise OSError("Input video file does not exist")
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if not os.path.isfile(args.target_net):
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raise OSError("Target Net does not exist")
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if not os.path.isfile(args.search_net):
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raise OSError("Search Net does not exist")
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if not os.path.isfile(args.rpn_head):
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raise OSError("RPN Head Net does not exist")
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#Load the Networks
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target_net = cv.dnn.readNetFromONNX(args.target_net)
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target_net.setPreferableBackend(args.backend)
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target_net.setPreferableTarget(args.target)
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search_net = cv.dnn.readNetFromONNX(args.search_net)
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search_net.setPreferableBackend(args.backend)
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search_net.setPreferableTarget(args.target)
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rpn_head = cv.dnn.readNetFromONNX(args.rpn_head)
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rpn_head.setPreferableBackend(args.backend)
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rpn_head.setPreferableTarget(args.target)
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model = ModelBuilder(target_net, search_net, rpn_head)
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tracker = SiamRPNTracker(model)
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first_frame = True
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cv.namedWindow('SiamRPN++ Tracker', cv.WINDOW_AUTOSIZE)
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for frame in get_frames(args.input_video):
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if first_frame:
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try:
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init_rect = cv.selectROI('SiamRPN++ Tracker', frame, False, False)
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except:
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exit()
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tracker.init(frame, init_rect)
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first_frame = False
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else:
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outputs = tracker.track(frame)
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bbox = list(map(int, outputs['bbox']))
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x,y,w,h = bbox
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cv.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 3)
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cv.imshow('SiamRPN++ Tracker', frame)
|
|
key = cv.waitKey(1)
|
|
if key == ord("q"):
|
|
break
|
|
|
|
if __name__ == '__main__':
|
|
main()
|