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demo_sort_yolov5.py
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demo_sort_yolov5.py
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# author: zerg
# libs
from multiprocessing import Process, Queue
from sklearn import preprocessing
import numpy as np
import cv2
import process
import torch
import copy
import glob
import os
import sys
import sort
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import (
check_img_size, non_max_suppression, apply_classifier, scale_coords,
xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
from utils.torch_utils import select_device, load_classifier, time_synchronized
# params
num_skip = 1 # for speed reason
name_window = 'frame'
# path_video = '/media/manu/samsung/videos/siamrpn/20200701.mp4'
# path_video = '/media/manu/samsung/videos/at2021/mp4/Video1.mp4'
# path_video = '/media/manu/samsung/videos/at2021/mp4/Video1年级.mp4'
# path_video = 'rtsp://192.168.3.233:554/live/ch2'
# path_video = 'rtsp://192.168.3.51:554/ch2'
path_video = 'rtsp://192.168.3.122:554/ch1'
weights = ['/home/manu/tmp/yolov5s_e300_ceil_relua_rfocus_synbn_weights-e300_r1-11/weights/best.pt', ]
device = torch.device('cuda:0')
conf_thres = 0.5
iou_thres = 0.5
classes = None
agnostic_nms = False
half = True
imgsz = 416
sort_max_age = 1
sort_min_hits = 3
sort_iou_threshold = 0.3
sort_max_track_num = 32
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better test mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
elif scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return img, ratio, (dw, dh)
if __name__ == '__main__':
print('tracker init start ...')
sort_colours = np.random.rand(sort_max_track_num, 3) * 255
mot_tracker = sort.Sort(max_age=sort_max_age,
min_hits=sort_min_hits,
iou_threshold=sort_iou_threshold) # create instance of the SORT tracker
print('tracker init done')
print('detect init start ...')
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
print('detect init done')
q_decoder = Queue()
p_decoder = Process(target=process.process_decoder, args=(q_decoder, path_video, num_skip), daemon=True)
p_decoder.start()
cv2.namedWindow(name_window, cv2.WINDOW_NORMAL)
cv2.resizeWindow(name_window, 960, 540)
face_recog_aligned_save_idx = 0
while True:
item_frame = q_decoder.get()
frame = item_frame[0]
img = letterbox(frame, new_shape=imgsz)[0]
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
img = np.ascontiguousarray(img)
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
t1 = time_synchronized()
pred = model(img, augment=False)[0]
pred = non_max_suppression(pred, conf_thres, iou_thres, classes=classes, agnostic=agnostic_nms)
det = pred[0]
if det is not None:
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], frame.shape).round()
det = det[:, 0:5].detach().cpu().numpy()
else:
det = np.empty((0, 5))
mot_tracker.update(det)
print('num of trackers --> %d' % len(mot_tracker.trackers))
if len(mot_tracker.trackers) > 0:
# plot
for track in mot_tracker.trackers:
d = track.get_state()[0]
bbox = np.concatenate((d, [track.id+1])).reshape(1, -1)
bbox = np.squeeze(bbox)
box = bbox.astype(int)
# print('score', faces[i][4])
# track = mot_tracker.trackers[len(faces) - i - 1] # reversed order
# box = faces[i].astype(int)
sort_id = box[4].astype(np.int32)
color_id = sort_colours[sort_id % sort_max_track_num, :]
cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), color_id, 2)
info = 'tid' + ' %d' % sort_id
fontScale = 1.2
cv2.putText(frame, info,
(box[0], box[1]+int(fontScale * 25)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, color_id, 2)
info = 'age' + ' %d' % track.age
fontScale = 1.2
cv2.putText(frame, info,
(box[0], box[1]+int(fontScale * 25 * 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, color_id, 2)
cv2.imshow(name_window, frame)
# if cv2.waitKey(1) & 0xFF == ord('q'):
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()