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synced 2026-07-30 07:43:03 +04:00
Merge remote-tracking branch 'upstream/3.4' into merge-3.4
This commit is contained in:
Alexander Alekhin
@@ -189,7 +189,10 @@ int main( int argc, const char** argv )
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const int EDGE_THRESHOLD_LOW = 50;
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const int EDGE_THRESHOLD_HIGH = 100;
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CommandLineParser parser(argc, argv, keys);
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parser.about("\nThis program demonstrates implementation of 'intelligent scissors' algorithm\n"
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parser.about("\nThis program demonstrates implementation of 'Intelligent Scissors' algorithm designed\n"
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"by Eric N. Mortensen and William A. Barrett, and described in article\n"
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"'Intelligent Scissors for Image Composition':\n"
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"http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.138.3811&rep=rep1&type=pdf\n"
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"To start drawing a new contour select a pixel, click LEFT mouse button.\n"
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"To fix a path click LEFT mouse button again.\n"
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"To finish drawing a contour click RIGHT mouse button.\n");
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@@ -0,0 +1,281 @@
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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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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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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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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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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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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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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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#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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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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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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#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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#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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#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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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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sz2 = (w + pad) * (h + pad)
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return np.sqrt(sz2)
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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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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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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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#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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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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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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#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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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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#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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if __name__ == "__main__":
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main()
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