405 lines
16 KiB
Python
405 lines
16 KiB
Python
|
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()
|