Hu Chunming / vpt_ascend

  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])
  
          # udpate 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()