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deep_list.py
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deep_list.py
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import argparse
import time
from pathlib import Path
import streamlit as st
import cv2
import torch
import torch.backends.cudnn as cudnn
import os
import sys
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
sys.path.insert(0, './yolov5') # Path for internal module without changing base
import numpy as np
from yolov5.models.common import DetectMultiBackend
from yolov5.utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from yolov5.utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from yolov5.utils.general import set_logging
from yolov5.utils.plots import Annotator, colors, save_one_box, plot_one_box
from yolov5.utils.torch_utils import select_device, time_sync
from deep_sort_pytorch.utils.parser import get_config
from deep_sort_pytorch.deep_sort import DeepSort
from graphs import bbox_rel,draw_boxes
from collections import Counter
import psutil
import subprocess
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0] # YOLOv5 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
def get_gpu_memory():
result = subprocess.check_output(
[
'nvidia-smi', '--query-gpu=memory.used',
'--format=csv,nounits,noheader'
], encoding='utf-8')
gpu_memory = [int(x) for x in result.strip().split('\n')]
return gpu_memory[0]
@torch.no_grad()
def detect(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
source=ROOT / 'yolov5/data/images', # file/dir/URL/glob, 0 for webcam
data=ROOT / 'yolov5/data/coco128.yaml', # dataset.yaml path
stframe=None,
#stgraph=None,
kpi1_text="",
kpi2_text="", kpi3_text="",
js1_text="",js2_text="",js3_text="",
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
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
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
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False,
display_labels=False,
config_deepsort="deep_sort_pytorch/configs/deep_sort.yaml", #Deep Sort configuration
conf_thres_drift = 0.75,
save_poor_frame__ = False,
inf_ov_1_text="", inf_ov_2_text="",inf_ov_3_text="", inf_ov_4_text="",
fps_warn="",fps_drop_warn_thresh=8
):
save_img = not nosave and not source.endswith('.txt') # save inference images
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
## initialize deepsort
cfg = get_config()
cfg.merge_from_file(config_deepsort)
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
# Directories
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
if save_poor_frame__:
try:
os.mkdir("drift_frames")
except:
print("Folder exists, overwriting...")
# Initialize
set_logging()
device = select_device(device)
half &= device.type != 'cpu' # half precision only supported on CUDA
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16 supported on limited backends with CUDA
if pt or jit:
model.model.half() if half else model.model.float()
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
# Dataloader
if webcam:
#view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
bs = len(dataset) # batch_size
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
t0 = time.time()
dt, seen = [0.0, 0.0, 0.0], 0
prev_time = time.time()
selected_names = names.copy()
global_graph_dict = dict()
global_drift_dict = dict()
test_drift = []
frame_num = -1
poor_perf_frame_counter=0
mapped_ = dict()
min_FPS = 10000
max_FPS = -1
for path, im, im0s, vid_cap, s in dataset:
frame_num = frame_num+1
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half 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
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
dt[2] += time_sync() - t3
# Process predictions
class_count = 0
drift_dict = dict()
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
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
# Print results
names_ = []
cnt = []
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
names_.append(names[int(c)])
cnt.append(int(n.detach().cpu().numpy()))
mapped_.update(dict(zip(names_, cnt)))
global_graph_dict = Counter(global_graph_dict) + Counter(mapped_)
bbox_xywh = []
confs = []
# Adapt detections to deep sort input format
for *xyxy, conf, cls in det:
x_c, y_c, bbox_w, bbox_h = bbox_rel(*xyxy)
obj = [x_c, y_c, bbox_w, bbox_h]
bbox_xywh.append(obj)
confs.append([conf.item()])
# print("conf : {0}, conf_t : {1}".format(conf, conf_thres))
if conf<conf_thres_drift:
if names[int(cls)] not in test_drift:
test_drift.append(names[int(cls)])
if save_poor_frame__:
cv2.imwrite("drift_frames/frame_{0}.png".format(frame_num), im0)
poor_perf_frame_counter+=1
# print(type(conf_thres))
xywhs = torch.Tensor(bbox_xywh)
confss = torch.Tensor(confs)
# Pass detections to deepsort
outputs = deepsort.update(xywhs, confss, im0)
# draw boxes for visualization
if len(outputs) > 0:
# print("Outputs :", outputs)
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
draw_boxes(im0, bbox_xyxy, identities)
# Write MOT compliant results to file
if save_txt and len(outputs) != 0:
for j, output in enumerate(outputs):
bbox_left = output[0]
bbox_top = output[1]
bbox_w = output[2]
bbox_h = output[3]
identity = output[-1]
with open(txt_path, 'a') as f:
f.write(('%g ' * 10 + '\n') % (frame_idx, identity, bbox_left,
bbox_top, bbox_w, bbox_h, -1, -1, -1, -1)) # label format
# Write results Label
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img or display_labels: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
else:
deepsort.increment_ages()
# Stream results
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.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, im0.shape[1], im0.shape[0]
save_path += '.mp4'
vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer.write(im0)
curr_time = time.time()
fps_ = curr_time - prev_time
fps_ = round(1/round(fps_, 3),1)
prev_time = curr_time
js1_text.write(str(psutil.virtual_memory()[2])+"%")
js2_text.write(str(psutil.cpu_percent())+'%')
js3_text.write(str(get_gpu_memory())+' MB')
kpi1_text.write(str(fps_)+' FPS')
if fps_ < fps_drop_warn_thresh:
fps_warn.warning(f"FPS dropped below {fps_drop_warn_thresh}")
kpi2_text.write(mapped_)
kpi3_text.write(global_graph_dict)
inf_ov_1_text.write(test_drift)
inf_ov_2_text.write(poor_perf_frame_counter)
if fps_<min_FPS:
inf_ov_3_text.write(fps_)
min_FPS = fps_
if fps_>max_FPS:
inf_ov_4_text.write(fps_)
max_FPS = fps_
stframe.image(im0, channels="BGR", use_column_width=True)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
print(f"Results saved to {save_dir}{s}")
if update:
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
if vid_cap:
vid_cap.release()