vd/triplet-loss-cars
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
vd
2022-09-09 22:29:25 +03:00
parent c81d9106d5
commit 9b469860fa
3 changed files with 7 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
.model_inference.py.swp
View File

Binary file not shown.

11
model_inference.py
View File

@@ -96,12 +96,12 @@ model_embedding.load_state_dict(torch.load('./embedding-output/model_embedding.p
model_embedding.to(device) model_embedding.to(device)
model_embedding.eval() model_embedding.eval()
with open('/model_classifier.obj','rb') as file: with open('./model_classifier.obj','rb') as file:
model_classifier = pickle.load(file) model_classifier = pickle.load(file)
classes = model_classifier.__getstate__()['classes_'] classes = model_classifier.__getstate__()['classes_']
video = Path('/content/test_videos_2022/2022-NLS-5-NLS_05_2022_Heli_UHD_01-000140-000155-Karussell.mp4') video = Path('./test_videos_2022/2022-NLS-5-NLS_05_2022_Heli_UHD_01-000140-000155-Karussell.mp4')
reader = cv2.VideoCapture(str(video)) reader = cv2.VideoCapture(str(video))
fps = reader.get(cv2.CAP_PROP_FPS) fps = reader.get(cv2.CAP_PROP_FPS)
@@ -109,18 +109,19 @@ w = int(reader.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(reader.get(cv2.CAP_PROP_FRAME_HEIGHT)) h = int(reader.get(cv2.CAP_PROP_FRAME_HEIGHT))
reader.release() reader.release()
imgsz = check_img_size((w, h), s=model.stride)
dataset = LoadImages(video, img_size=imgsz, stride=model.stride, auto=model.pt)
weights_path = Path('./yolov5/best.pt') weights_path = Path('./yolov5/best.pt')
model = DetectMultiBackend(weights_path, device=torch.device(device)) model = DetectMultiBackend(weights_path, device=torch.device(device))
imgsz = check_img_size((w, h), s=model.stride)
dataset = LoadImages(video, img_size=imgsz, stride=model.stride, auto=model.pt)
save_dir = Path('./detection-output/') save_dir = Path('./detection-output/')
os.makedirs(save_dir) os.makedirs(save_dir)
writer = cv2.VideoWriter(str(save_dir / 'res.mp4'), cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) writer = cv2.VideoWriter(str(save_dir / 'res.mp4'), cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
for frame_n, (path, im, im0s, vid_cap, s) in enumerate(dataset): for frame_n, (path, im, im0s, vid_cap, s) in enumerate(dataset):
print(frame_n)
im = torch.from_numpy(im).to(device) im = torch.from_numpy(im).to(device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0 im /= 255 # 0 - 255 to 0.0 - 1.0

1
requirements.txt
View File

@@ -1,4 +1,5 @@
gdown gdown
seaborn
torch torch
torchvision torchvision
sklearn sklearn