mirror of https://github.com/opencv/opencv.git
Alexander Alekhin
5 years ago
commit
4cdb4652cf
14 changed files with 553 additions and 93 deletions
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""" |
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DaSiamRPN tracker. |
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Original paper: https://arxiv.org/abs/1808.06048 |
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Link to original repo: https://github.com/foolwood/DaSiamRPN |
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Links to onnx models: |
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network: https://www.dropbox.com/s/rr1lk9355vzolqv/dasiamrpn_model.onnx?dl=0 |
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kernel_r1: https://www.dropbox.com/s/999cqx5zrfi7w4p/dasiamrpn_kernel_r1.onnx?dl=0 |
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kernel_cls1: https://www.dropbox.com/s/qvmtszx5h339a0w/dasiamrpn_kernel_cls1.onnx?dl=0 |
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""" |
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|
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import numpy as np |
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import cv2 as cv |
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import argparse |
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import sys |
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|
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class DaSiamRPNTracker: |
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#initialization of used values, initial bounding box, used network |
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def __init__(self, im, target_pos, target_sz, net, kernel_r1, kernel_cls1): |
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self.windowing = "cosine" |
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self.exemplar_size = 127 |
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self.instance_size = 271 |
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self.total_stride = 8 |
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self.score_size = (self.instance_size - self.exemplar_size) // self.total_stride + 1 |
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self.context_amount = 0.5 |
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self.ratios = [0.33, 0.5, 1, 2, 3] |
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self.scales = [8, ] |
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self.anchor_num = len(self.ratios) * len(self.scales) |
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self.penalty_k = 0.055 |
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self.window_influence = 0.42 |
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self.lr = 0.295 |
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self.im_h = im.shape[0] |
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self.im_w = im.shape[1] |
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self.target_pos = target_pos |
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self.target_sz = target_sz |
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self.avg_chans = np.mean(im, axis=(0, 1)) |
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self.net = net |
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self.score = [] |
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|
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if ((self.target_sz[0] * self.target_sz[1]) / float(self.im_h * self.im_w)) < 0.004: |
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raise AssertionError("Initializing BB is too small-try to restart tracker with larger BB") |
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|
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self.anchor = self.__generate_anchor() |
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wc_z = self.target_sz[0] + self.context_amount * sum(self.target_sz) |
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hc_z = self.target_sz[1] + self.context_amount * sum(self.target_sz) |
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s_z = round(np.sqrt(wc_z * hc_z)) |
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|
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z_crop = self.__get_subwindow_tracking(im, self.exemplar_size, s_z) |
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z_crop = z_crop.transpose(2, 0, 1).reshape(1, 3, 127, 127).astype(np.float32) |
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self.net.setInput(z_crop) |
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z_f = self.net.forward('63') |
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kernel_r1.setInput(z_f) |
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r1 = kernel_r1.forward() |
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kernel_cls1.setInput(z_f) |
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cls1 = kernel_cls1.forward() |
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r1 = r1.reshape(20, 256, 4, 4) |
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cls1 = cls1.reshape(10, 256 , 4, 4) |
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self.net.setParam(self.net.getLayerId('65'), 0, r1) |
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self.net.setParam(self.net.getLayerId('68'), 0, cls1) |
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|
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if self.windowing == "cosine": |
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self.window = np.outer(np.hanning(self.score_size), np.hanning(self.score_size)) |
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elif self.windowing == "uniform": |
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self.window = np.ones((self.score_size, self.score_size)) |
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self.window = np.tile(self.window.flatten(), self.anchor_num) |
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|
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#creating anchor for tracking bounding box |
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def __generate_anchor(self): |
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self.anchor = np.zeros((self.anchor_num, 4), dtype = np.float32) |
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size = self.total_stride * self.total_stride |
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count = 0 |
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|
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for ratio in self.ratios: |
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ws = int(np.sqrt(size / ratio)) |
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hs = int(ws * ratio) |
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for scale in self.scales: |
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wws = ws * scale |
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hhs = hs * scale |
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self.anchor[count] = [0, 0, wws, hhs] |
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count += 1 |
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|
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score_sz = int(self.score_size) |
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self.anchor = np.tile(self.anchor, score_sz * score_sz).reshape((-1, 4)) |
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ori = - (score_sz / 2) * self.total_stride |
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xx, yy = np.meshgrid([ori + self.total_stride * dx for dx in range(score_sz)], [ori + self.total_stride * dy for dy in range(score_sz)]) |
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xx, yy = np.tile(xx.flatten(), (self.anchor_num, 1)).flatten(), np.tile(yy.flatten(), (self.anchor_num, 1)).flatten() |
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self.anchor[:, 0], self.anchor[:, 1] = xx.astype(np.float32), yy.astype(np.float32) |
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return self.anchor |
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|
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#track function |
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def track(self, im): |
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wc_z = self.target_sz[1] + self.context_amount * sum(self.target_sz) |
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hc_z = self.target_sz[0] + self.context_amount * sum(self.target_sz) |
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s_z = np.sqrt(wc_z * hc_z) |
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scale_z = self.exemplar_size / s_z |
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d_search = (self.instance_size - self.exemplar_size) / 2 |
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pad = d_search / scale_z |
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s_x = round(s_z + 2 * pad) |
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|
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#region preprocessing |
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x_crop = self.__get_subwindow_tracking(im, self.instance_size, s_x) |
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x_crop = x_crop.transpose(2, 0, 1).reshape(1, 3, 271, 271).astype(np.float32) |
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self.score = self.__tracker_eval(x_crop, scale_z) |
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self.target_pos[0] = max(0, min(self.im_w, self.target_pos[0])) |
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self.target_pos[1] = max(0, min(self.im_h, self.target_pos[1])) |
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self.target_sz[0] = max(10, min(self.im_w, self.target_sz[0])) |
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self.target_sz[1] = max(10, min(self.im_h, self.target_sz[1])) |
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|
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#update bounding box position |
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def __tracker_eval(self, x_crop, scale_z): |
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target_size = self.target_sz * scale_z |
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self.net.setInput(x_crop) |
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outNames = self.net.getUnconnectedOutLayersNames() |
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outNames = ['66', '68'] |
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delta, score = self.net.forward(outNames) |
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delta = np.transpose(delta, (1, 2, 3, 0)) |
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delta = np.ascontiguousarray(delta, dtype = np.float32) |
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delta = np.reshape(delta, (4, -1)) |
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score = np.transpose(score, (1, 2, 3, 0)) |
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score = np.ascontiguousarray(score, dtype = np.float32) |
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score = np.reshape(score, (2, -1)) |
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score = self.__softmax(score)[1, :] |
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delta[0, :] = delta[0, :] * self.anchor[:, 2] + self.anchor[:, 0] |
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delta[1, :] = delta[1, :] * self.anchor[:, 3] + self.anchor[:, 1] |
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delta[2, :] = np.exp(delta[2, :]) * self.anchor[:, 2] |
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delta[3, :] = np.exp(delta[3, :]) * self.anchor[:, 3] |
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|
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def __change(r): |
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return np.maximum(r, 1./r) |
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|
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def __sz(w, h): |
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pad = (w + h) * 0.5 |
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sz2 = (w + pad) * (h + pad) |
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return np.sqrt(sz2) |
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|
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def __sz_wh(wh): |
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pad = (wh[0] + wh[1]) * 0.5 |
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sz2 = (wh[0] + pad) * (wh[1] + pad) |
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return np.sqrt(sz2) |
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|
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s_c = __change(__sz(delta[2, :], delta[3, :]) / (__sz_wh(target_size))) |
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r_c = __change((target_size[0] / target_size[1]) / (delta[2, :] / delta[3, :])) |
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penalty = np.exp(-(r_c * s_c - 1.) * self.penalty_k) |
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pscore = penalty * score |
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pscore = pscore * (1 - self.window_influence) + self.window * self.window_influence |
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best_pscore_id = np.argmax(pscore) |
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target = delta[:, best_pscore_id] / scale_z |
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target_size /= scale_z |
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lr = penalty[best_pscore_id] * score[best_pscore_id] * self.lr |
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res_x = target[0] + self.target_pos[0] |
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res_y = target[1] + self.target_pos[1] |
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res_w = target_size[0] * (1 - lr) + target[2] * lr |
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res_h = target_size[1] * (1 - lr) + target[3] * lr |
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self.target_pos = np.array([res_x, res_y]) |
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self.target_sz = np.array([res_w, res_h]) |
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return score[best_pscore_id] |
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|
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def __softmax(self, x): |
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x_max = x.max(0) |
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e_x = np.exp(x - x_max) |
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y = e_x / e_x.sum(axis = 0) |
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return y |
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|
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#evaluations with cropped image |
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def __get_subwindow_tracking(self, im, model_size, original_sz): |
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im_sz = im.shape |
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c = (original_sz + 1) / 2 |
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context_xmin = round(self.target_pos[0] - c) |
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context_xmax = context_xmin + original_sz - 1 |
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context_ymin = round(self.target_pos[1] - c) |
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context_ymax = context_ymin + original_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_sz[1] + 1)) |
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bottom_pad = int(max(0., context_ymax - im_sz[0] + 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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r, c, k = im.shape |
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|
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if any([top_pad, bottom_pad, left_pad, right_pad]): |
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te_im = np.zeros((r + top_pad + bottom_pad, c + left_pad + right_pad, k), np.uint8) |
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te_im[top_pad:top_pad + r, left_pad:left_pad + c, :] = im |
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if top_pad: |
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te_im[0:top_pad, left_pad:left_pad + c, :] = self.avg_chans |
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if bottom_pad: |
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te_im[r + top_pad:, left_pad:left_pad + c, :] = self.avg_chans |
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if left_pad: |
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te_im[:, 0:left_pad, :] = self.avg_chans |
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if right_pad: |
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te_im[:, c + left_pad:, :] = self.avg_chans |
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im_patch_original = te_im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] |
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else: |
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im_patch_original = im[int(context_ymin):int(context_ymax + 1), int(context_xmin):int(context_xmax + 1), :] |
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|
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if not np.array_equal(model_size, original_sz): |
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im_patch_original = cv.resize(im_patch_original, (model_size, model_size)) |
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return im_patch_original |
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|
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#function for reading paths, bounding box drawing, showing results |
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def main(): |
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parser = argparse.ArgumentParser(description="Run tracker") |
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parser.add_argument("--net", type=str, default="dasiamrpn_model.onnx", help="Full path to onnx model of net") |
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parser.add_argument("--kernel_r1", type=str, default="dasiamrpn_kernel_r1.onnx", help="Full path to onnx model of kernel_r1") |
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parser.add_argument("--kernel_cls1", type=str, default="dasiamrpn_kernel_cls1.onnx", help="Full path to onnx model of kernel_cls1") |
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parser.add_argument("--input", type=str, help="Full path to input. Do not use if input is camera") |
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args = parser.parse_args() |
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point1 = () |
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point2 = () |
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mark = True |
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drawing = False |
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cx, cy, w, h = 0.0, 0.0, 0, 0 |
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|
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def get_bb(event, x, y, flag, param): |
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nonlocal point1, point2, cx, cy, w, h, drawing, mark |
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if event == cv.EVENT_LBUTTONDOWN: |
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if not drawing: |
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drawing = True |
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point1 = (x, y) |
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else: |
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drawing = False |
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elif event == cv.EVENT_MOUSEMOVE: |
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if drawing: |
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point2 = (x, y) |
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elif event == cv.EVENT_LBUTTONUP: |
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cx = point1[0] - (point1[0] - point2[0]) / 2 |
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cy = point1[1] - (point1[1] - point2[1]) / 2 |
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w = abs(point1[0] - point2[0]) |
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h = abs(point1[1] - point2[1]) |
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mark = False |
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#loading network`s and kernel`s models |
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net = cv.dnn.readNet(args.net) |
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kernel_r1 = cv.dnn.readNet(args.kernel_r1) |
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kernel_cls1 = cv.dnn.readNet(args.kernel_cls1) |
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#initializing bounding box |
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cap = cv.VideoCapture(args.input if args.input else 0) |
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cv.namedWindow("DaSiamRPN") |
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cv.setMouseCallback("DaSiamRPN", get_bb) |
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whitespace_key = 32 |
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while cv.waitKey(40) != whitespace_key: |
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has_frame, frame = cap.read() |
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if not has_frame: |
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sys.exit(0) |
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cv.imshow("DaSiamRPN", frame) |
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while mark: |
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twin = np.copy(frame) |
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if point1 and point2: |
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cv.rectangle(twin, point1, point2, (0, 255, 255), 3) |
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cv.imshow("DaSiamRPN", twin) |
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cv.waitKey(40) |
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target_pos, target_sz = np.array([cx, cy]), np.array([w, h]) |
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tracker = DaSiamRPNTracker(frame, target_pos, target_sz, net, kernel_r1, kernel_cls1) |
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|
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#tracking loop |
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while cap.isOpened(): |
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has_frame, frame = cap.read() |
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if not has_frame: |
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sys.exit(0) |
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tracker.track(frame) |
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w, h = tracker.target_sz |
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cx, cy = tracker.target_pos |
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cv.rectangle(frame, (int(cx - w // 2), int(cy - h // 2)), (int(cx - w // 2) + int(w), int(cy - h // 2) + int(h)),(0, 255, 255), 3) |
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cv.imshow("DaSiamRPN", frame) |
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key = cv.waitKey(1) |
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if key == ord("q"): |
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break |
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cap.release() |
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cv.destroyAllWindows() |
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|
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if __name__ == "__main__": |
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main() |
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