183 lines
7.1 KiB
Python
Executable File
183 lines
7.1 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(
|
|
net_config_path,
|
|
data_config_path,
|
|
weights_file_path,
|
|
images_path,
|
|
output='output',
|
|
batch_size=16,
|
|
img_size=416,
|
|
conf_thres=0.3,
|
|
nms_thres=0.45,
|
|
save_txt=False,
|
|
save_images=False,
|
|
):
|
|
device = torch_utils.select_device()
|
|
print("Using device: \"{}\"".format(device))
|
|
|
|
os.system('rm -rf ' + output)
|
|
os.makedirs(output, exist_ok=True)
|
|
|
|
data_config = parse_data_config(data_config_path)
|
|
|
|
# Load model
|
|
model = Darknet(net_config_path, img_size)
|
|
|
|
if weights_file_path.endswith('.pt'): # pytorch format
|
|
if weights_file_path.endswith('weights/yolov3.pt') and not os.path.isfile(weights_file_path):
|
|
os.system('wget https://storage.googleapis.com/ultralytics/yolov3.pt -O ' + weights_file_path)
|
|
checkpoint = torch.load(weights_file_path, map_location='cpu')
|
|
model.load_state_dict(checkpoint['model'])
|
|
del checkpoint
|
|
else: # darknet format
|
|
load_weights(model, weights_file_path)
|
|
|
|
# current = model.state_dict()
|
|
# saved = checkpoint['model']
|
|
# # 1. filter out unnecessary keys
|
|
# saved = {k: v for k, v in saved.items() if ((k in current) and (current[k].shape == v.shape))}
|
|
# # 2. overwrite entries in the existing state dict
|
|
# current.update(saved)
|
|
# # 3. load the new state dict
|
|
# model.load_state_dict(current)
|
|
# model.to(device).eval()
|
|
# del checkpoint, current, saved
|
|
|
|
model.to(device).eval()
|
|
|
|
# Set Dataloader
|
|
classes = load_classes(data_config['names']) # Extracts class labels from file
|
|
dataloader = load_images(images_path, batch_size=batch_size, img_size=img_size)
|
|
|
|
imgs = [] # Stores image paths
|
|
img_detections = [] # Stores detections for each image index
|
|
prev_time = time.time()
|
|
for i, (img_paths, img) in enumerate(dataloader):
|
|
print('%g/%g' % (i + 1, len(dataloader)), end=' ')
|
|
|
|
# Get detections
|
|
with torch.no_grad():
|
|
# cv2.imwrite('zidane_416.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1]) # letterboxed
|
|
img = torch.from_numpy(img).unsqueeze(0).to(device)
|
|
if ONNX_EXPORT:
|
|
pred = 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)
|
|
img_detections.extend(detections)
|
|
imgs.extend(img_paths)
|
|
|
|
print('Batch %d... Done. (%.3fs)' % (i, time.time() - prev_time))
|
|
prev_time = time.time()
|
|
|
|
# Bounding-box colors
|
|
color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))]
|
|
|
|
if len(img_detections) == 0:
|
|
return
|
|
|
|
# Iterate through images and save plot of detections
|
|
for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
|
|
print("image %g: '%s'" % (img_i, path))
|
|
|
|
# Draw bounding boxes and labels of detections
|
|
if detections is not None:
|
|
img = cv2.imread(path)
|
|
|
|
# The amount of padding that was added
|
|
pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))
|
|
pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))
|
|
# Image height and width after padding is removed
|
|
unpad_h = img_size - pad_y
|
|
unpad_w = img_size - pad_x
|
|
|
|
unique_classes = detections[:, -1].cpu().unique()
|
|
bbox_colors = random.sample(color_list, len(unique_classes))
|
|
|
|
# write results to .txt file
|
|
results_img_path = os.path.join(output, path.split('/')[-1])
|
|
results_txt_path = results_img_path + '.txt'
|
|
if os.path.isfile(results_txt_path):
|
|
os.remove(results_txt_path)
|
|
|
|
for i in unique_classes:
|
|
n = (detections[:, -1].cpu() == i).sum()
|
|
print('%g %ss' % (n, classes[int(i)]))
|
|
|
|
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
|
|
# Rescale coordinates to original dimensions
|
|
box_h = ((y2 - y1) / unpad_h) * img.shape[0]
|
|
box_w = ((x2 - x1) / unpad_w) * img.shape[1]
|
|
y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round().item()
|
|
x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item()
|
|
x2 = (x1 + box_w).round().item()
|
|
y2 = (y1 + box_h).round().item()
|
|
x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0)
|
|
|
|
# write to file
|
|
if save_txt:
|
|
with open(results_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 the bbox to the plot
|
|
label = '%s %.2f' % (classes[int(cls_pred)], conf)
|
|
color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
|
|
plot_one_box([x1, y1, x2, y2], img, label=label, color=color)
|
|
|
|
if save_images:
|
|
# Save generated image with detections
|
|
cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img)
|
|
|
|
if platform == 'darwin': # MacOS (local)
|
|
os.system('open ' + output)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
# Get data configuration
|
|
|
|
parser.add_argument('--image-folder', type=str, default='data/samples', help='path to images')
|
|
parser.add_argument('--output-folder', type=str, default='output', help='path to outputs')
|
|
parser.add_argument('--plot-flag', type=bool, default=True)
|
|
parser.add_argument('--txt-out', type=bool, default=False)
|
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
|
parser.add_argument('--data-config', type=str, default='cfg/coco.data', help='path to data config file')
|
|
parser.add_argument('--weights', type=str, default='weights/yolov3.pt', help='path to weights file')
|
|
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')
|
|
parser.add_argument('--batch-size', type=int, default=1, help='size of the batches')
|
|
parser.add_argument('--img-size', type=int, default=32 * 13, help='size of each image dimension')
|
|
opt = parser.parse_args()
|
|
print(opt)
|
|
|
|
torch.cuda.empty_cache()
|
|
|
|
init_seeds()
|
|
|
|
detect(
|
|
opt.cfg,
|
|
opt.data_config,
|
|
opt.weights,
|
|
opt.image_folder,
|
|
output=opt.output_folder,
|
|
batch_size=opt.batch_size,
|
|
img_size=opt.img_size,
|
|
conf_thres=opt.conf_thres,
|
|
nms_thres=opt.nms_thres,
|
|
save_txt=opt.txt_out,
|
|
save_images=opt.plot_flag,
|
|
)
|