def main(model_number):
model = models[model_number]
cfg = get_config(model['model_cfg'])
detector = ObjectDetector(
cfg,
os.path.join(
'resource',
model['checkpoint']
),
device_id=0
)
image = cv2.imread('image.jpg')
for _ in trange(NUM_WARM, desc='Warm-up'):
detector.predict(image, threshold=0.3)
start = time.time()
for _ in trange(NUM_RUNS, desc='Benches'):
detector.predict(image, threshold=0.3)
torch.cuda.synchronize()
total = time.time() - start
print(f'Performance: {NUM_RUNS / total:0.2f} rps')
class TrafficSignDetector(object):
def __init__(self):
self.detector = ObjectDetector()
self.classifier = Classifier()
def detect(self, image):
objects = time.measure(lambda: self.detector.predict(image),
'detection')
extend_bounding_boxes(objects, 0.15)
images = time.measure(
lambda: prepare_for_classification(objects, image),
'image preprocessing')
labels = time.measure(lambda: self.classifier.predict(images),
'classification')
print(objects, labels)
return objects, labels
def detect_multiple(self, images):
objects, preprocessed_images, box_scales = time.measure(
lambda: self.detector.predict_multiple(images), 'detection')
if len(objects[0]) == 0:
return [[]], [[]]
preprocessed = time.measure(
lambda: resize_for_classification(objects, preprocessed_images,
images), 'preprocessing')
labels = time.measure(lambda: self.classifier.predict(preprocessed),
'classification')
results = []
j = 0
for i in range(0, len(images)):
results.append([labels[k] for k in range(j, j + len(objects[i]))])
j += len(objects[i])
r_objects = []
for i in range(0, len(objects)):
r_objects.append([])
objs = objects[i]
r_objects[i] = [[obj[0], *obj[1:] * box_scales[i]] for obj in objs]
print(r_objects, results)
return r_objects, results