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