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import argparse | |
import os | |
import platform | |
import sys | |
from pathlib import Path | |
import math | |
import torch | |
import numpy as np | |
from deep_sort_pytorch.utils.parser import get_config | |
from deep_sort_pytorch.deep_sort import DeepSort | |
from collections import deque | |
FILE = Path(__file__).resolve() | |
ROOT = FILE.parents[0] # YOLO root directory | |
if str(ROOT) not in sys.path: | |
sys.path.append(str(ROOT)) # add ROOT to PATH | |
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative | |
from models.common import DetectMultiBackend | |
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams | |
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, | |
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) | |
from utils.plots import Annotator, colors, save_one_box | |
from utils.torch_utils import select_device, smart_inference_mode | |
def initialize_deepsort(): | |
# Create the Deep SORT configuration object and load settings from the YAML file | |
cfg_deep = get_config() | |
cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") | |
# Initialize the DeepSort tracker | |
deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT, | |
max_dist=cfg_deep.DEEPSORT.MAX_DIST, | |
# min_confidence parameter sets the minimum tracking confidence required for an object detection to be considered in the tracking process | |
min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, | |
#nms_max_overlap specifies the maximum allowed overlap between bounding boxes during non-maximum suppression (NMS) | |
nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, | |
#max_iou_distance parameter defines the maximum intersection-over-union (IoU) distance between object detections | |
max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, | |
# Max_age: If an object's tracking ID is lost (i.e., the object is no longer detected), this parameter determines how many frames the tracker should wait before assigning a new id | |
max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, | |
#nn_budget: It sets the budget for the nearest-neighbor search. | |
nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, | |
use_cuda=False | |
) | |
return deepsort | |
deepsort = initialize_deepsort() | |
data_deque = {} | |
def classNames(): | |
cocoClassNames = ["Bus", "Bike", "Car", "Pedestrian", "Truck" | |
] | |
return cocoClassNames | |
className = classNames() | |
def colorLabels(classid): | |
if classid == 0: #person | |
color = (85, 45, 255) | |
elif classid == 1: #car | |
color = (222, 82, 175) | |
elif classid == 2: #Motorbike | |
color = (0, 204, 255) | |
elif classid == 3: #Bus | |
color = (0,149,255) | |
else: | |
color = (200, 100,0) | |
return tuple(color) | |
def draw_boxes(frame, bbox_xyxy, draw_trails, identities=None, categories=None, offset=(0,0)): | |
height, width, _ = frame.shape | |
for key in list(data_deque): | |
if key not in identities: | |
data_deque.pop(key) | |
for i, box in enumerate(bbox_xyxy): | |
x1, y1, x2, y2 = [int(i) for i in box] | |
x1 += offset[0] | |
y1 += offset[0] | |
x2 += offset[0] | |
y2 += offset[0] | |
#Find the center point of the bounding box | |
center = int((x1+x2)/2), int((y1+y2)/2) | |
cat = int(categories[i]) if categories is not None else 0 | |
color = colorLabels(cat) | |
#color = [255,0,0]#compute_color_labels(cat) | |
id = int(identities[i]) if identities is not None else 0 | |
# create new buffer for new object | |
if id not in data_deque: | |
data_deque[id] = deque(maxlen= 64) | |
data_deque[id].appendleft(center) | |
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) | |
name = className[cat] | |
label = str(id) + ":" + name | |
text_size = cv2.getTextSize(label, 0, fontScale=0.5, thickness=2)[0] | |
c2 = x1 + text_size[0], y1 - text_size[1] - 3 | |
cv2.rectangle(frame, (x1, y1), c2, color, -1) | |
cv2.putText(frame, label, (x1, y1 - 2), 0, 0.5, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA) | |
cv2.circle(frame,center, 2, (0,255,0), cv2.FILLED) | |
if draw_trails: | |
# draw trail | |
for i in range(1, len(data_deque[id])): | |
# check if on buffer value is none | |
if data_deque[id][i - 1] is None or data_deque[id][i] is None: | |
continue | |
# generate dynamic thickness of trails | |
thickness = int(np.sqrt(64 / float(i + i)) * 1.5) | |
# draw trails | |
cv2.line(frame, data_deque[id][i - 1], data_deque[id][i], color, thickness) | |
return frame | |
def run_deepsort( | |
weights=ROOT / 'yolo.pt', # model path or triton URL | |
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) | |
data=ROOT / 'data/coco.yaml', # dataset.yaml path | |
imgsz=(640, 640), # inference size (height, width) | |
conf_thres=0.25, # confidence threshold | |
iou_thres=0.45, # NMS IOU threshold | |
max_det=1000, # maximum detections per image | |
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu | |
view_img=False, # show results | |
nosave=False, # do not save images/videos | |
classes=None, # filter by class: --class 0, or --class 0 2 3 | |
agnostic_nms=False, # class-agnostic NMS | |
augment=False, # augmented inference | |
visualize=False, # visualize features | |
update=False, # update all models | |
project=ROOT / 'runs/detect', # save results to project/name | |
name='exp', # save results to project/name | |
exist_ok=False, # existing project/name ok, do not increment | |
half=False, # use FP16 half-precision inference | |
dnn=False, # use OpenCV DNN for ONNX inference | |
vid_stride=1, # video frame-rate stride | |
draw_trails = False, | |
): | |
source = str(source) | |
save_img = not nosave and not source.endswith('.txt') # save inference images | |
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) | |
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) | |
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) | |
screenshot = source.lower().startswith('screen') | |
if is_url and is_file: | |
source = check_file(source) # download | |
# Directories | |
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run | |
save_dir.mkdir(parents=True, exist_ok=True) # make dir | |
# Load model | |
device = select_device(device) | |
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) | |
stride, names, pt = model.stride, model.names, model.pt | |
imgsz = check_img_size(imgsz, s=stride) # check image size | |
# Dataloader | |
bs = 1 # batch_size | |
if webcam: | |
view_img = check_imshow(warn=True) | |
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) | |
bs = len(dataset) | |
elif screenshot: | |
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) | |
else: | |
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) | |
vid_path, vid_writer = [None] * bs, [None] * bs | |
# Run inference | |
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup | |
seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) | |
for path, im, im0s, vid_cap, s in dataset: | |
with dt[0]: | |
im = torch.from_numpy(im).to(model.device) | |
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 | |
im /= 255 # 0 - 255 to 0.0 - 1.0 | |
if len(im.shape) == 3: | |
im = im[None] # expand for batch dim | |
# Inference | |
with dt[1]: | |
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False | |
pred = model(im, augment=augment, visualize=visualize) | |
pred = pred[0][0] | |
# NMS | |
with dt[2]: | |
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) | |
# Second-stage classifier (optional) | |
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) | |
# Process predictions | |
for i, det in enumerate(pred): # per image | |
seen += 1 | |
if webcam: # batch_size >= 1 | |
p, im0, frame = path[i], im0s[i].copy(), dataset.count | |
s += f'{i}: ' | |
else: | |
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) | |
p = Path(p) # to Path | |
save_path = str(save_dir / p.name) # im.jpg | |
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt | |
s += '%gx%g ' % im.shape[2:] # print string | |
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh | |
ims = im0.copy() | |
if len(det): | |
# Rescale boxes from img_size to im0 size | |
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() | |
# Print results | |
for c in det[:, 5].unique(): | |
n = (det[:, 5] == c).sum() # detections per class | |
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string | |
xywh_bboxs = [] | |
confs = [] | |
oids = [] | |
outputs = [] | |
# Write results | |
for *xyxy, conf, cls in reversed(det): | |
x1, y1, x2, y2 = xyxy | |
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) | |
#Find the Center Coordinates for each of the detected object | |
cx, cy = int((x1+x2)/2), int((y1+y2)/2) | |
#Find the Width and Height of the Boundng box | |
bbox_width = abs(x1-x2) | |
bbox_height = abs(y1-y2) | |
xcycwh = [cx, cy, bbox_width, bbox_height] | |
xywh_bboxs.append(xcycwh) | |
conf = math.ceil(conf*100)/100 | |
confs.append(conf) | |
classNameInt = int(cls) | |
oids.append(classNameInt) | |
xywhs = torch.tensor(xywh_bboxs) | |
confss = torch.tensor(confs) | |
outputs = deepsort.update(xywhs, confss, oids, ims) | |
if len(outputs) > 0: | |
bbox_xyxy = outputs[:, :4] | |
identities = outputs[:, -2] | |
object_id = outputs[:, -1] | |
draw_boxes(ims, bbox_xyxy, draw_trails, identities, object_id) | |
# Stream results | |
if view_img: | |
if platform.system() == 'Linux' and p not in windows: | |
windows.append(p) | |
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) | |
cv2.resizeWindow(str(p), ims.shape[1], ims.shape[0]) | |
cv2.imshow(str(p), ims) | |
cv2.waitKey(1) # 1 millisecond | |
# Save results (image with detections) | |
if save_img: | |
if vid_path[i] != save_path: # new video | |
vid_path[i] = save_path | |
if isinstance(vid_writer[i], cv2.VideoWriter): | |
vid_writer[i].release() # release previous video writer | |
if vid_cap: # video | |
fps = vid_cap.get(cv2.CAP_PROP_FPS) | |
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
else: # stream | |
fps, w, h = 30, ims.shape[1], ims.shape[0] | |
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos | |
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) | |
vid_writer[i].write(ims) | |
# Print time (inference-only) | |
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") | |
if update: | |
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) | |
return save_path | |
def parse_opt(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL') | |
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') | |
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') | |
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') | |
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') | |
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') | |
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') | |
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') | |
parser.add_argument('--view-img', action='store_true', help='show results') | |
parser.add_argument('--nosave', action='store_true', help='do not save images/videos') | |
parser.add_argument('--draw-trails', action='store_true', help='do not drawtrails') | |
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') | |
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') | |
parser.add_argument('--augment', action='store_true', help='augmented inference') | |
parser.add_argument('--visualize', action='store_true', help='visualize features') | |
parser.add_argument('--update', action='store_true', help='update all models') | |
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') | |
parser.add_argument('--name', default='exp', help='save results to project/name') | |
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') | |
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') | |
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') | |
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') | |
opt = parser.parse_args() | |
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand | |
print_args(vars(opt)) | |
return opt | |
def main(opt): | |
# check_requirements(exclude=('tensorboard', 'thop')) | |
run_deepsort(**vars(opt)) | |
if __name__ == "__main__": | |
opt = parse_opt() | |
main(opt) |