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