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Cascadercnn_deepsort.py
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Cascadercnn_deepsort.py
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import os
import cv2
import time
import argparse
import torch
import warnings
import numpy as np
from detector import build_detector
from deep_sort import build_tracker
from utils.draw import draw_boxes
from utils.parser import get_config
from utils.log import get_logger
from utils.io import write_results
import glob
class VideoTracker(object):
def __init__(self, cfg, args, video_path):
self.cfg = cfg
self.args = args
self.video_path = video_path
self.logger = get_logger("root")
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
if args.cam != -1:
print("Using webcam " + str(args.cam))
self.vdo = cv2.VideoCapture(args.cam)
else:
self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda, detector_name='EfficientDet')
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
# self.class_names = self.detector.class_names
def __enter__(self):
if self.args.cam != -1:
ret, frame = self.vdo.read()
assert ret, "Error: Camera error"
self.im_width = frame.shape[0]
self.im_height = frame.shape[1]
else:
assert os.path.isfile(self.video_path), "Path error"
self.vdo.open(self.video_path)
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
assert self.vdo.isOpened()
if self.args.save_path:
os.makedirs(self.args.save_path, exist_ok=True)
# path of saved video and results
self.save_video_path = os.path.join(self.args.save_path, "results.avi")
self.save_results_path = os.path.join(self.args.save_path, "results.txt")
# create video writer
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 20, (self.im_width,self.im_height))
# logging
self.logger.info("Save results to {}".format(self.args.save_path))
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
if exc_type:
print(exc_type, exc_value, exc_traceback)
def run(self):
results = []
idx_frame = 0
while self.vdo.grab():
idx_frame += 1
if idx_frame % self.args.frame_interval:
continue
start = time.time()
_, ori_im = self.vdo.retrieve()
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
# print(im)
# do detection
bbox_xywh, cls_conf, cls_ids = self.detector(im)
# print(bbox_xywh, cls_conf)
# select person class
mask = cls_ids==0
bbox_xywh = bbox_xywh[mask]
# bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
bbox_xywh[:,3:] *= 1.2
cls_conf = cls_conf[mask]
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
# do tracking
outputs = self.deepsort.update(bbox_xywh, cls_conf, im)
# draw boxes for visualization
if len(outputs) > 0:
bbox_tlwh = []
bbox_xyxy = outputs[:,:4]
identities = outputs[:,-1]
ori_im = draw_boxes(ori_im, bbox_xyxy, identities)
for bb_xyxy in bbox_xyxy:
bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy))
results.append((idx_frame-1, bbox_tlwh, identities))
end = time.time()
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
if self.args.save_path:
self.writer.write(ori_im)
# save results
write_results(self.save_results_path, results, 'mot')
# logging
self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \
.format(end-start, 1/(end-start), bbox_xywh.shape[0], len(outputs)))
class ImageTracker(object):
def __init__(self, cfg, args, image_path, save_filename, im_width, im_height):
self.cfg = cfg
self.args = args
self.image_path = image_path
self.logger = get_logger("root")
self.save_filename = save_filename
self.im_width = im_width
self.im_height = im_height
use_cuda = args.use_cuda and torch.cuda.is_available()
if not use_cuda:
warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
if args.display:
cv2.namedWindow("test", cv2.WINDOW_NORMAL)
cv2.resizeWindow("test", args.display_width, args.display_height)
# if args.cam != -1:
# print("Using webcam " + str(args.cam))
# self.vdo = cv2.VideoCapture(args.cam)
# else:
# self.vdo = cv2.VideoCapture()
self.detector = build_detector(cfg, use_cuda=use_cuda, detector_name='Rcnn')
#print(self.detector)
self.deepsort = build_tracker(cfg, use_cuda=use_cuda)
# self.class_names = self.detector.class_names
def __enter__(self):
# if self.args.cam != -1:
# ret, frame = self.vdo.read()
# assert ret, "Error: Camera error"
# self.im_width = frame.shape[0]
# self.im_height = frame.shape[1]
# else:
# assert os.path.isfile(self.video_path), "Path error"
# self.vdo.open(self.video_path)
# self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
# self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
# assert self.vdo.isOpened()
# self.images = glob.glob(self.image_path+'/*.jpg')
image_format = ['.jpg', '.jpeg', '.png', '.tif']
self.images = sorted(glob.glob('%s/*.*' % self.image_path))
self.images = list(filter(lambda x: os.path.splitext(x)[1].lower() in image_format, self.images))
#print(self.images)
if self.args.save_path:
os.makedirs(self.args.save_path, exist_ok=True)
# path of saved video and results
self.save_video_path = os.path.join(self.args.save_path, self.save_filename+".avi")
self.save_results_path = os.path.join(self.args.save_path, self.save_filename+'.txt')
#print(self.save_results_path)
# create video writer
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 20, (self.im_width,self.im_height))
# logging
self.logger.info("Save results to {}".format(self.args.save_path))
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
if exc_type:
print(exc_type, exc_value, exc_traceback)
def run(self):
results = []
idx_frame = 0
for img_file in self.images:
print(img_file)
idx_frame += 1
# if idx_frame % self.args.frame_interval:
# continue
start = time.time()
# print(img_file)
ori_im = cv2.imread(img_file)
im = ori_im
# im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
# im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
# print(im)
# do detection
#print(self.detector(im))
bbox_xywh, cls_conf, cls_ids = self.detector(im)
#print(bbox_xywh)
# select person class
mask = cls_ids==0
# print(bbox_xywh)
bbox_xywh = bbox_xywh[mask]
# print(len(bbox_xywh))
# bbox dilation just in case bbox too small, delete this line if using a better pedestrian detector
#bbox_xywh[:,3:] *= 1.05
cls_conf = cls_conf[mask]
im = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB)
# do tracking
outputs = self.deepsort.update(bbox_xywh, cls_conf, im)
# draw boxes for visualization
if len(outputs) > 0:
bbox_tlwh = []
bbox_xyxy = outputs[:,:4]
identities = outputs[:,-1]
ori_im = draw_boxes(ori_im, bbox_xyxy, identities)
for bb_xyxy in bbox_xyxy:
bbox_tlwh.append(self.deepsort._xyxy_to_tlwh(bb_xyxy))
results.append((idx_frame-2, bbox_tlwh, identities))
end = time.time()
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
if self.args.save_path:
self.writer.write(ori_im)
if self.args.display:
cv2.imshow("test", ori_im)
cv2.waitKey(1)
# save results
write_results(self.save_results_path, results, 'mot')
# logging
self.logger.info("time: {:.03f}s, fps: {:.03f}, detection numbers: {}, tracking numbers: {}" \
.format(end-start, 1/(end-start), bbox_xywh.shape[0], len(outputs)))
def parse_args():
parser = argparse.ArgumentParser()
# parser.add_argument("VIDEO_PATH", type=str)
parser.add_argument("--config_detection", type=str, default="./configs/rcnn.yaml")
parser.add_argument("--config_deepsort", type=str, default="./configs/deep_sort.yaml")
# parser.add_argument("--ignore_display", dest="display", action="store_false", default=True)
parser.add_argument("--display", action="store_true")
parser.add_argument("--frame_interval", type=int, default=1)
parser.add_argument("--display_width", type=int, default=800)
parser.add_argument("--display_height", type=int, default=600)
parser.add_argument("--save_path", type=str, default="./outputs/")
parser.add_argument("--cpu", dest="use_cuda", action="store_false", default=True)
parser.add_argument("--camera", action="store", dest="cam", type=int, default="-1")
return parser.parse_args()
if __name__=="__main__":
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
args = parse_args()
cfg = get_config()
cfg.merge_from_file(args.config_detection)
cfg.merge_from_file(args.config_deepsort)
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_pytorch-master/detector/Pedestron/datasets/zhongxing/test/Track1/img1', 'Track1', 1550, 734) as img_trk:
# img_trk.run()
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_pytorch-master/detector/Pedestron/datasets/zhongxing/test/Track4/img1', 'Track4', 1920, 980) as img_trk:
# img_trk.run()
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_pytorch-master/detector/Pedestron/datasets/zhongxing/test/Track5/img1', 'Track5', 1400, 559) as img_trk:
# img_trk.run()
#
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_pytorch-master/detector/Pedestron/datasets/zhongxing/test/Track9/img1', 'Track9', 1116, 874) as img_trk:
# img_trk.run()
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_pytorch-master/detector/Pedestron/datasets/zhongxing/test/Track10/img1', 'Track10', 615, 593) as img_trk:
# img_trk.run()
with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_new/detector/Pedestron/datasets/zhongxing/testB/Track2/new_img1', 'Track2', 1550, 734) as img_trk:
img_trk.run()
#
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_new/detector/Pedestron/datasets/zhongxing/testB/Track3/new_img1', 'Track3', 1116, 874) as img_trk:
# img_trk.run()
#
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_new/detector/Pedestron/datasets/zhongxing/testB/Track6/new_img1', 'Track6', 1400, 559) as img_trk:
# img_trk.run()
# #
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_new/detector/Pedestron/datasets/zhongxing/testB/Track8/new_img1', 'Track8', 928, 620) as img_trk:
# img_trk.run()
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_new/detector/Pedestron/datasets/zhongxing/testB/Track11/new_img1', 'Track11', 615, 593) as img_trk:
# img_trk.run()
# #
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/deep_sort_new/detector/Pedestron/datasets/zhongxing/testB/Track12/new_img1', 'Track12', 1728, 824) as img_trk:
# img_trk.run()
# with ImageTracker(cfg, args, r'/media/shenfei/shensdd/10millon_data/test/11_Train_Station_Square', '11_Train_Station_Square', 26583, 14957) as img_trk:
# img_trk.run()