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