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demo.py
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demo.py
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import argparse
import cv2
import os
import time
import numpy as np
import imageio
import torch
# load transform
from dataset.build import build_transform
# load some utils
from utils.misc import load_weight
from utils.box_ops import rescale_bboxes
from utils.vis_tools import visualize
from models.detectors import build_model
from config import build_model_config, build_trans_config, build_dataset_config
def parse_args():
parser = argparse.ArgumentParser(description='Real-time Object Detection LAB')
# Basic setting
parser.add_argument('-size', '--img_size', default=640, type=int,
help='the max size of input image')
parser.add_argument('--mosaic', default=None, type=float,
help='mosaic augmentation.')
parser.add_argument('--mixup', default=None, type=float,
help='mixup augmentation.')
parser.add_argument('--mode', default='image',
type=str, help='Use the data from image, video or camera')
parser.add_argument('--cuda', action='store_true', default=False,
help='Use cuda')
parser.add_argument('--path_to_img', default='dataset/demo/images/',
type=str, help='The path to image files')
parser.add_argument('--path_to_vid', default='dataset/demo/videos/',
type=str, help='The path to video files')
parser.add_argument('--path_to_save', default='det_results/demos/',
type=str, help='The path to save the detection results')
parser.add_argument('--show', action='store_true', default=False,
help='show visualization')
parser.add_argument('--gif', action='store_true', default=False,
help='generate gif.')
# Model setting
parser.add_argument('-m', '--model', default='yolov1', type=str,
help='build yolo')
parser.add_argument('-nc', '--num_classes', default=80, type=int,
help='number of classes.')
parser.add_argument('--weight', default=None,
type=str, help='Trained state_dict file path to open')
parser.add_argument('-ct', '--conf_thresh', default=0.35, type=float,
help='confidence threshold')
parser.add_argument('-nt', '--nms_thresh', default=0.5, type=float,
help='NMS threshold')
parser.add_argument('--topk', default=100, type=int,
help='topk candidates dets of each level before NMS')
parser.add_argument("--deploy", action="store_true", default=False,
help="deploy mode or not")
parser.add_argument('--fuse_conv_bn', action='store_true', default=False,
help='fuse Conv & BN')
parser.add_argument('--no_multi_labels', action='store_true', default=False,
help='Perform post-process with multi-labels trick.')
parser.add_argument('--nms_class_agnostic', action='store_true', default=False,
help='Perform NMS operations regardless of category.')
# Data setting
parser.add_argument('-d', '--dataset', default='coco',
help='coco, voc, crowdhuman, widerface.')
return parser.parse_args()
def detect(args,
model,
device,
transform,
num_classes,
class_names,
class_indexs,
mode='image'):
# class color
np.random.seed(0)
class_colors = [(np.random.randint(255),
np.random.randint(255),
np.random.randint(255)) for _ in range(num_classes)]
save_path = os.path.join(args.path_to_save, mode)
os.makedirs(save_path, exist_ok=True)
# ------------------------- Camera ----------------------------
if mode == 'camera':
print('use camera !!!')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
save_size = (640, 480)
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S',time.localtime(time.time()))
save_video_name = os.path.join(save_path, cur_time+'.avi')
fps = 15.0
out = cv2.VideoWriter(save_video_name, fourcc, fps, save_size)
print(save_video_name)
image_list = []
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
while True:
ret, frame = cap.read()
if ret:
if cv2.waitKey(1) == ord('q'):
break
orig_h, orig_w, _ = frame.shape
# prepare
x, _, ratio = transform(frame)
x = x.unsqueeze(0).to(device)
# inference
t0 = time.time()
outputs = model(x)
scores = outputs['scores']
labels = outputs['labels']
bboxes = outputs['bboxes']
t1 = time.time()
print("Infer time: {:.1f} ms. ".format((t1 - t0) * 1000))
# rescale bboxes
bboxes = rescale_bboxes(bboxes, [orig_w, orig_h], ratio)
# vis detection
frame_vis = visualize(image=frame,
bboxes=bboxes,
scores=scores,
labels=labels,
class_colors=class_colors,
class_names=class_names,
class_indexs=class_indexs)
frame_resized = cv2.resize(frame_vis, save_size)
out.write(frame_resized)
if args.gif:
gif_resized = cv2.resize(frame, (640, 480))
gif_resized_rgb = gif_resized[..., (2, 1, 0)]
image_list.append(gif_resized_rgb)
if args.show:
cv2.imshow('detection', frame_resized)
cv2.waitKey(1)
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
# generate GIF
if args.gif:
save_gif_path = os.path.join(save_path, 'gif_files')
os.makedirs(save_gif_path, exist_ok=True)
save_gif_name = os.path.join(save_gif_path, '{}.gif'.format(cur_time))
print('generating GIF ...')
imageio.mimsave(save_gif_name, image_list, fps=fps)
print('GIF done: {}'.format(save_gif_name))
# ------------------------- Video ---------------------------
elif mode == 'video':
video = cv2.VideoCapture(args.path_to_vid)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
save_size = (640, 480)
cur_time = time.strftime('%Y-%m-%d-%H-%M-%S',time.localtime(time.time()))
save_video_name = os.path.join(save_path, cur_time+'.avi')
fps = 15.0
out = cv2.VideoWriter(save_video_name, fourcc, fps, save_size)
print(save_video_name)
image_list = []
while(True):
ret, frame = video.read()
if ret:
# ------------------------- Detection ---------------------------
orig_h, orig_w, _ = frame.shape
# prepare
x, _, ratio = transform(frame)
x = x.unsqueeze(0).to(device)
# inference
t0 = time.time()
outputs = model(x)
scores = outputs['scores']
labels = outputs['labels']
bboxes = outputs['bboxes']
t1 = time.time()
print("Infer time: {:.1f} ms. ".format((t1 - t0) * 1000))
# rescale bboxes
bboxes = rescale_bboxes(bboxes, [orig_w, orig_h], ratio)
# vis detection
frame_vis = visualize(image=frame,
bboxes=bboxes,
scores=scores,
labels=labels,
class_colors=class_colors,
class_names=class_names,
class_indexs=class_indexs)
frame_resized = cv2.resize(frame_vis, save_size)
out.write(frame_resized)
if args.gif:
gif_resized = cv2.resize(frame, (640, 480))
gif_resized_rgb = gif_resized[..., (2, 1, 0)]
image_list.append(gif_resized_rgb)
if args.show:
cv2.imshow('detection', frame_resized)
cv2.waitKey(1)
else:
break
video.release()
out.release()
cv2.destroyAllWindows()
# generate GIF
if args.gif:
save_gif_path = os.path.join(save_path, 'gif_files')
os.makedirs(save_gif_path, exist_ok=True)
save_gif_name = os.path.join(save_gif_path, '{}.gif'.format(cur_time))
print('generating GIF ...')
imageio.mimsave(save_gif_name, image_list, fps=fps)
print('GIF done: {}'.format(save_gif_name))
# ------------------------- Image ----------------------------
elif mode == 'image':
for i, img_id in enumerate(os.listdir(args.path_to_img)):
image = cv2.imread((args.path_to_img + '/' + img_id), cv2.IMREAD_COLOR)
orig_h, orig_w, _ = image.shape
# prepare
x, _, ratio = transform(image)
x = x.unsqueeze(0).to(device)
# inference
t0 = time.time()
outputs = model(x)
scores = outputs['scores']
labels = outputs['labels']
bboxes = outputs['bboxes']
t1 = time.time()
print("Infer time: {:.1f} ms. ".format((t1 - t0) * 1000))
# rescale bboxes
bboxes = rescale_bboxes(bboxes, [orig_w, orig_h], ratio)
# vis detection
img_processed = visualize(image=image,
bboxes=bboxes,
scores=scores,
labels=labels,
class_colors=class_colors,
class_names=class_names,
class_indexs=class_indexs)
cv2.imwrite(os.path.join(save_path, str(i).zfill(6)+'.jpg'), img_processed)
if args.show:
cv2.imshow('detection', img_processed)
cv2.waitKey(0)
def run():
args = parse_args()
# cuda
if args.cuda:
print('use cuda')
device = torch.device("cuda")
else:
device = torch.device("cpu")
# config
model_cfg = build_model_config(args)
trans_cfg = build_trans_config(model_cfg['trans_type'])
data_cfg = build_dataset_config(args)
## Data info
num_classes = data_cfg['num_classes']
class_names = data_cfg['class_names']
class_indexs = data_cfg['class_indexs']
# build model
model = build_model(args, model_cfg, device, num_classes, False)
# load trained weight
model = load_weight(model, args.weight, args.fuse_conv_bn)
model.to(device).eval()
# transform
val_transform, trans_cfg = build_transform(args, trans_cfg, model_cfg['max_stride'], is_train=False)
print("================= DETECT =================")
# run
detect(args = args,
mode = args.mode,
model = model,
device = device,
transform = val_transform,
num_classes = num_classes,
class_names = class_names,
class_indexs = class_indexs,
)
if __name__ == '__main__':
run()