111 lines
4.0 KiB
Python
Executable File
111 lines
4.0 KiB
Python
Executable File
import argparse
|
|
import time
|
|
|
|
from models import *
|
|
from utils.datasets import *
|
|
from utils.utils import *
|
|
|
|
from utils import torch_utils
|
|
|
|
|
|
def detect(
|
|
cfg,
|
|
weights,
|
|
images,
|
|
output='output',
|
|
img_size=416,
|
|
conf_thres=0.3,
|
|
nms_thres=0.45,
|
|
save_txt=False,
|
|
save_images=True
|
|
):
|
|
device = torch_utils.select_device()
|
|
os.system('rm -rf ' + output)
|
|
os.makedirs(output, exist_ok=True)
|
|
|
|
# Load model
|
|
model = Darknet(cfg, img_size)
|
|
|
|
if weights.endswith('.pt'): # pytorch format
|
|
if weights.endswith('weights/yolov3.pt') and not os.path.isfile(weights):
|
|
os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights)
|
|
model.load_state_dict(torch.load(weights, map_location='cpu')['model'])
|
|
else: # darknet format
|
|
load_darknet_weights(model, weights)
|
|
|
|
model.to(device).eval()
|
|
|
|
# Set Dataloader
|
|
dataloader = load_images(images, img_size=img_size)
|
|
|
|
# Get classes and colors
|
|
classes = load_classes(parse_data_cfg('cfg/coco.data')['names'])
|
|
colors = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
|
|
|
|
for i, (path, img, im0) in enumerate(dataloader):
|
|
print("%g/%g '%s': " % (i + 1, len(dataloader), path), end='')
|
|
t = time.time()
|
|
|
|
# Get detections
|
|
img = torch.from_numpy(img).unsqueeze(0).to(device)
|
|
if ONNX_EXPORT:
|
|
torch.onnx._export(model, img, 'weights/model.onnx', verbose=True)
|
|
return # ONNX export
|
|
pred = model(img)
|
|
pred = pred[pred[:, :, 4] > conf_thres]
|
|
|
|
if len(pred) > 0:
|
|
detections = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0]
|
|
|
|
# Draw bounding boxes and labels of detections
|
|
if detections is not None:
|
|
save_img_path = os.path.join(output, path.split('/')[-1])
|
|
save_txt_path = save_img_path + '.txt'
|
|
|
|
# Rescale boxes from 416 to true image size
|
|
detections[:, :4] = scale_coords(img_size, detections[:, :4], im0.shape)
|
|
|
|
unique_classes = detections[:, -1].cpu().unique()
|
|
for i in unique_classes:
|
|
n = (detections[:, -1].cpu() == i).sum()
|
|
print('%g %ss' % (n, classes[int(i)]), end=', ')
|
|
|
|
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
|
|
if save_txt: # Write to file
|
|
with open(save_txt_path, 'a') as file:
|
|
file.write('%g %g %g %g %g %g\n' % (x1, y1, x2, y2, cls_pred, cls_conf * conf))
|
|
|
|
if save_images: # Add bbox to the image
|
|
label = '%s %.2f' % (classes[int(cls_pred)], conf)
|
|
plot_one_box([x1, y1, x2, y2], im0, label=label, color=colors[int(cls_pred)])
|
|
|
|
if save_images: # Save generated image with detections
|
|
cv2.imwrite(save_img_path, im0)
|
|
|
|
print(' Done. (%.3fs)' % (time.time() - t))
|
|
|
|
if platform == 'darwin': # MacOS
|
|
os.system('open ' + output + '&& open ' + save_img_path)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
|
parser.add_argument('--weights', type=str, default='weights/yolov3.pt', help='path to weights file')
|
|
parser.add_argument('--images', type=str, default='data/samples', help='path to images')
|
|
parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
|
|
parser.add_argument('--conf-thres', type=float, default=0.50, help='object confidence threshold')
|
|
parser.add_argument('--nms-thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
|
|
opt = parser.parse_args()
|
|
print(opt)
|
|
|
|
with torch.no_grad():
|
|
detect(
|
|
opt.cfg,
|
|
opt.weights,
|
|
opt.images,
|
|
img_size=opt.img_size,
|
|
conf_thres=opt.conf_thres,
|
|
nms_thres=opt.nms_thres
|
|
)
|