--- /dev/null
+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,h,w = bbox
+ self.center_pos = np.array([x + (h - 1) / 2, y + (w - 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)
+ # 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)
+
+ 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" % 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" % 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()
projects / platform / upstream / opencv.git / commitdiff