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detect.py
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detect.py
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import argparse
import os
import shutil
import time
from pathlib import Path
import cv2
import torch
from numpy import random
from models.experimental import attempt_load
from utils.datasets import LoadImages
from utils.general import (
check_img_size, non_max_suppression, apply_classifier, scale_coords,
xyxy2xywh, plot_one_box, strip_optimizer, set_logging, removeAllFile)
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(save_img=False):
out, source, weights, imgsz = \
opt.output, opt.source, opt.weights, opt.img_size
# Initialize
set_logging()
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
# 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
# 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']) # load weights
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) #if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
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)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Write results
img2 = im0.copy()
nperson=[]
nname=[]
for *xyxy, conf, cls in reversed(det):
if save_img : # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
########################################################################################################
##classes 변수 생성 (이름)
classes = names[int(cls)]
##classes 변수 함수에 추가
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3, classes=classes)
##사람이라고 판단한 물체의 각 좌표 리스트에 저장
if classes == 'person':
nperson.append([int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])])
if classes == 'name_tag':
nname.append([int(xyxy[0]),int(xyxy[1]),int(xyxy[2]),int(xyxy[3])])
##사람이 아닌 리스트의 크기가 0보다 클 때 미리 복사해둔 프레임의 구역으로 이미지 덮기
if len(nname) >0:
for ii in range(len(nname)):
for pi in range(len(nperson)):
if nname[ii][1]>=nperson[pi][1] and nname[ii][3]<=nperson[pi][3] and nname[ii][0]>=nperson[pi][0] and nname[ii][2]<=nperson[pi][2]:
proi=img2[nname[ii][1]:nname[ii][3],nname[ii][0]:nname[ii][2]]
cv2.imwrite("./temp/{0}_{1}_{2}_{3}.jpg".format(nname[ii][1],nname[ii][3],nname[ii][0],nname[ii][2]),proi)
roi = img2[nperson[pi][1]:nperson[pi][3], nperson[pi][0]:nperson[pi][2]]
im0[nperson[pi][1]:nperson[pi][3], nperson[pi][0]:nperson[pi][2]] = roi
########################################################################################################
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Save results (image with detections)
if save_img:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = 'mp4v' # output video codec
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))
vid_writer = cv2.VideoWriter('./inference/output/output.mp4', cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer.write(im0)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='person600_deleted_epoch20.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--save-conf', action='store_true', help='output confidences in --save-txt labels')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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('--update', action='store_true', help='update all models')
opt = parser.parse_args()
print(opt)
detect()
# removeAllFile('./temp')