127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
import argparse
|
|
import shutil
|
|
import time
|
|
from pathlib import Path
|
|
from sys import platform
|
|
|
|
from models import *
|
|
from utils.datasets import *
|
|
from utils.utils import *
|
|
|
|
|
|
def detect(
|
|
cfg,
|
|
weights,
|
|
images,
|
|
output='output', # output folder
|
|
img_size=416,
|
|
conf_thres=0.3,
|
|
nms_thres=0.45,
|
|
save_txt=False,
|
|
save_images=True,
|
|
webcam=False
|
|
):
|
|
device = torch_utils.select_device()
|
|
if os.path.exists(output):
|
|
shutil.rmtree(output) # delete output folder
|
|
os.makedirs(output) # make new output folder
|
|
|
|
# Initialize model
|
|
model = Darknet(cfg, img_size)
|
|
|
|
# Load weights
|
|
if weights.endswith('.pt'): # pytorch format
|
|
if weights.endswith('yolov3.pt') and not os.path.exists(weights):
|
|
if (platform == 'darwin') or (platform == 'linux'):
|
|
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
|
|
if webcam:
|
|
save_images = False
|
|
dataloader = LoadWebcam(img_size=img_size)
|
|
else:
|
|
dataloader = LoadImages(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):
|
|
t = time.time()
|
|
if webcam:
|
|
print('webcam frame %g: ' % (i + 1), end='')
|
|
else:
|
|
print('image %g/%g %s: ' % (i + 1, len(dataloader), path), end='')
|
|
save_path = str(Path(output) / Path(path).name)
|
|
|
|
# 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
|
|
pred = model(img)
|
|
pred = pred[pred[:, :, 4] > conf_thres] # remove boxes < threshold
|
|
|
|
if len(pred) > 0:
|
|
# Run NMS on predictions
|
|
detections = non_max_suppression(pred.unsqueeze(0), conf_thres, nms_thres)[0]
|
|
|
|
# Rescale boxes from 416 to true image size
|
|
scale_coords(img_size, detections[:, :4], im0.shape).round()
|
|
|
|
# Print results to screen
|
|
unique_classes = detections[:, -1].cpu().unique()
|
|
for c in unique_classes:
|
|
n = (detections[:, -1].cpu() == c).sum()
|
|
print('%g %ss' % (n, classes[int(c)]), end=', ')
|
|
|
|
# Draw bounding boxes and labels of detections
|
|
for x1, y1, x2, y2, conf, cls_conf, cls in detections:
|
|
if save_txt: # Write to file
|
|
with open(save_path + '.txt', 'a') as file:
|
|
file.write('%g %g %g %g %g %g\n' %
|
|
(x1, y1, x2, y2, cls, cls_conf * conf))
|
|
|
|
# Add bbox to the image
|
|
label = '%s %.2f' % (classes[int(cls)], conf)
|
|
plot_one_box([x1, y1, x2, y2], im0, label=label, color=colors[int(cls)])
|
|
|
|
dt = time.time() - t
|
|
print('Done. (%.3fs)' % dt)
|
|
|
|
if save_images: # Save generated image with detections
|
|
cv2.imwrite(save_path, im0)
|
|
|
|
if webcam: # Show live webcam
|
|
cv2.imshow(weights, im0)
|
|
|
|
if save_images and (platform == 'darwin'): # linux/macos
|
|
os.system('open ' + output + ' ' + save_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.weights', 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
|
|
)
|