How to use tracker_builder method in tempest

Best Python code snippet using tempest_python

pysot_trackers.py

Source:pysot_trackers.py

1import sys2sys.path.append("backends/pysot")34from argparse import Namespace5import torch6from pathlib import Path7import numpy as np8import cv29import sys1011import pysot.models.model_builder12import pysot.tracker.tracker_builder13from pysot.core.config import cfg14#from pysot.models.model_builder import ModelBuilder15#from pysot.tracker.tracker_builder import build_tracker161718class SiamRPNTracker:19    def __init__(self, is_cpu=True):20        is_cpu = not torch.cuda.is_available() and is_cpu21        cfgarg = Namespace(config="backends/pysot/experiments/siamrpn_alex_dwxcorr/config.yaml",22                           snapshot="backends/pysot/experiments/siamrpn_alex_dwxcorr/model.pth",23                          )24        # try:  # åæåãåºæ¥ãªãè¬ä»æ§ã®ãã25        #     del sys.modules['pysot.core.config']26        #     del sys.modules['pysot.models.model_builder']27        #     del sys.modules['pysot.tracker.tracker_builder']28        # except Exception as e:29        #     print("cannot re import")30        # from pysot.core.config import cfg31        # import pysot.models.model_builder32        # import pysot.tracker.tracker_builder3334        cfg.merge_from_file(cfgarg.config)35        cfg.CUDA = not is_cpu36        #cfg.CUDA = False37        device = torch.device("cuda" if not is_cpu else "cpu")38        model = pysot.models.model_builder.ModelBuilder()39        model.load_state_dict(torch.load(cfgarg.snapshot,40                              map_location=lambda storage, loc: storage.cpu()))41        model.eval().to(device)42        self.tracker = pysot.tracker.tracker_builder.build_tracker(model)43        44    def set_bbox(self, image, xyxy_dict=None, polygon_list=None):45        if xyxy_dict is None:46            raise Exception("SiameseMaskTracker set_bbox need xyxy_dict")47        x1 = xyxy_dict["x1"]48        y1 = xyxy_dict["y1"]49        x2 = xyxy_dict["x2"]50        y2 = xyxy_dict["y2"]51        w = x2-x152        h = y2-y153        54        self.tracker.init(image, (x1,y1,w,h))55        56    def get_bbox(self, image):57        outputs = self.tracker.track(image)58        59        xyxy_dict = {"x1":int(outputs["bbox"][0]),60                     "y1":int(outputs["bbox"][1]),61                     "x2":int(outputs["bbox"][0]+outputs["bbox"][2]),62                     "y2":int(outputs["bbox"][1]+outputs["bbox"][3])63                    }64        65        out_dict = {"bbox_dict":xyxy_dict, "polygon":None}66        return out_dict676869class SiamRPNTrackerV2:70    def __init__(self, is_cpu=True):71        is_cpu = not torch.cuda.is_available() and is_cpu72        cfgarg = Namespace(config="backends/pysot/experiments/siamrpn_mobilev2_l234_dwxcorr/config.yaml",73                           snapshot="backends/pysot/experiments/siamrpn_mobilev2_l234_dwxcorr/model.pth",74                          )7576        # try:  # åæåãåºæ¥ãªãè¬ä»æ§ã®ãã77        #     del sys.modules['pysot.core.config']78        #     del sys.modules['pysot.models.model_builder']79        #     del sys.modules['pysot.tracker.tracker_builder']80        # except Exception as e:81        #     print("cannot re import")82        # from pysot.core.config import cfg83        # import pysot.models.model_builder84        # import pysot.tracker.tracker_builder8586        # cfg.merge_from_file(cfgarg.config)87        cfg.CUDA = not is_cpu88        #cfg.CUDA = False89        device = torch.device("cuda" if not is_cpu else "cpu")90        model = pysot.models.model_builder.ModelBuilder()91        model.load_state_dict(torch.load(cfgarg.snapshot,92                              map_location=lambda storage, loc: storage.cpu()))93        model.eval().to(device)94        self.tracker = pysot.tracker.tracker_builder.build_tracker(model)95        96    def set_bbox(self, image, xyxy_dict=None, polygon_list=None):97        if xyxy_dict is None:98            raise Exception("SiameseMaskTracker set_bbox need xyxy_dict")99        x1 = xyxy_dict["x1"]100        y1 = xyxy_dict["y1"]101        x2 = xyxy_dict["x2"]102        y2 = xyxy_dict["y2"]103        w = x2-x1104        h = y2-y1105        106        self.tracker.init(image, (x1,y1,w,h))107        108    def get_bbox(self, image):109        outputs = self.tracker.track(image)110        111        xyxy_dict = {"x1":int(outputs["bbox"][0]),112                     "y1":int(outputs["bbox"][1]),113                     "x2":int(outputs["bbox"][0]+outputs["bbox"][2]),114                     "y2":int(outputs["bbox"][1]+outputs["bbox"][3])115                    }116        117        out_dict = {"bbox_dict":xyxy_dict, "polygon":None}118        return out_dict119120121class SiamMaskTrackerV2():122    def __init__(self, is_cpu=True, epsilon=0.01):123        is_cpu = not torch.cuda.is_available() and is_cpu124        cfgarg = Namespace(config="backends/pysot/experiments/siammask_r50_l3/config.yaml",125                           snapshot="backends/pysot/experiments/siammask_r50_l3/model.pth",126                          )127128        # try:  # åæåãåºæ¥ãªãè¬ä»æ§ã®ãã129        #     del sys.modules['pysot.core.config']130        #     del sys.modules['pysot.models.model_builder']131        #     del sys.modules['pysot.tracker.tracker_builder']132        # except Exception as e:133        #     print("cannot re import")134        # from pysot.core.config import cfg135        # import pysot.models.model_builder136        # import pysot.tracker.tracker_builder137138        cfg.merge_from_file(cfgarg.config)139        cfg.CUDA = not is_cpu140        #cfg.CUDA = False141        device = torch.device("cuda" if not is_cpu else "cpu")142        model = pysot.models.model_builder.ModelBuilder()143        model.load_state_dict(torch.load(cfgarg.snapshot,144                              map_location=lambda storage, loc: storage.cpu()))145        model.eval().to(device)146        self.tracker = pysot.tracker.tracker_builder.build_tracker(model)147        self.epsilon = epsilon148        149    def set_bbox(self, image, xyxy_dict=None, polygon_list=None):150        if xyxy_dict is None:151            raise Exception("SiameseMaskTracker set_bbox need xyxy_dict")152        x1 = xyxy_dict["x1"]153        y1 = xyxy_dict["y1"]154        x2 = xyxy_dict["x2"]155        y2 = xyxy_dict["y2"]156        w = x2-x1157        h = y2-y1158        159        self.tracker.init(image, (x1,y1,w,h))160        161    def get_bbox(self, image):162        outputs = self.tracker.track(image)163        mask_img = (outputs["mask"]*255).astype(np.uint8)164        _, img_otsu = cv2.threshold(mask_img, 0, 255, cv2.THRESH_OTSU)165        #_, contours, _ = cv2.findContours(img_otsu, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)166        if cv2.__version__[-5] == '4':167            contours, _ = cv2.findContours(img_otsu, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)168        else:169            _, contours, _ = cv2.findContours(img_otsu, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)170        out_dict = {}171        172        cnt_area = [cv2.contourArea(cnt) for cnt in contours]173        if len(contours) != 0 and np.max(cnt_area) > 100:174            contour = contours[np.argmax(cnt_area)]  # use max area polygon175            polygon = contour.reshape(-1, 2)176            pbox = cv2.boundingRect(polygon)  # Min Max Rectangle177178            out_dict["bbox_dict"] = {"x1":pbox[0],179                                     "y1":pbox[1],180                                     "x2":pbox[0]+pbox[2],181                                     "y2":pbox[1]+pbox[3]182                                    }           183            epsilon = self.epsilon*cv2.arcLength(polygon, True)184            approx = cv2.approxPolyDP(polygon, epsilon, True).squeeze()185            out_dict["polygon"] = [{"x":int(one_point[0]), "y":int(one_point[1])} for one_point in approx]186            187        else:188            out_dict["polygon"] = None189            out_dict["bbox_dict"] = {"x1":None,"y1":None,"x2":None,"y2":None}190        191        return out_dict192193    194if __name__ == "__main__":
...

builder.py

Source:builder.py

1# -*- coding: utf-8 -*2from typing import Dict3from yacs.config import CfgNode4from .tracker import builder as tracker_builder5from .tracker.tracker_base import TRACK_PIPELINES6# from .segmenter import builder as segmenter_builder7def build_pipeline(task: str, cfg: CfgNode):8    """9    Builder function.10    Arguments11    ---------12    task: str13        builder task name (track|vos)14    cfg: CfgNode15        buidler configuration16    Returns17    -------18    torch.nn.Module19        module built by builder20    """21    if task == "track":22        track_pipeline = tracker_builder.build(cfg)23        return track_pipeline24    else:25        print("model for task {} is not complted".format(task))26        exit(-1)27def get_config() -> Dict[str, CfgNode]:28    """29    Get available component list config30    Returns31    -------32    CfgNode33        config with list of available components34    """35    cfg_dict = {"track": CfgNode()}36    for cfg_name, module in zip([37            "track",38    ], [39            TRACK_PIPELINES,40    ]):41        cfg = cfg_dict[cfg_name]42        cfg["name"] = "unknown"43        for name in module:44            cfg[name] = CfgNode()45            tester = module[name]46            hps = tester.default_hyper_params47            for hp_name in hps:48                cfg[name][hp_name] = hps[hp_name]...

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