def detect(target: str, language: str, dir: str, camera: str, path: str):
"""
Take feed from camera and detect vehicle (using TVM opt model),
draw bounding box, and read license plate (based on the language)
Parameters
----------
target: str
The device (CPU or GPU) that TVM will inference on
language: str
Region of the license plate that OpenALPR will detect
dir: str
Directory that has shared library, graphs, and params
camera: str
Specified camera input to use
path: str
Path to OpenALPR folder
"""
alpr = get_alpr(language, path)
ctx = tvm.context(target, 0)
if ctx.exist:
graph, lib, params = build(dir)
else:
print("Target does not exist")
sys.exit(1)
print("Starting video stream...")
cap = VideoCaptureThreading("/dev/video" + camera)
cap.start()
module = graph_runtime.create(graph, lib, ctx)
module.load_params(params)
fps = FPS()
fps = fps.start()
while True:
_, frame, oframe, x, img = cap.read()
class_IDs, scores, bounding_boxs = run(x, module, ctx)
class_IDs, bounding_boxs, scores = convertAsNumpy(
class_IDs, bounding_boxs, scores)
oframe = draw_plates(class_IDs, scores, bounding_boxs, oframe, img,
alpr)
cv2.imshow("frame", oframe)
if cv2.waitKey(1) & 0xFF == ord("q"):
cap.stop()
break
fps.update()
fps.stop()
print("Approx. FPS: {:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
def __init__(self, module, ctx, alpr, x=None, img=None, oframe=None):
self.x = x
self.img = img
self.alpr = alpr
self.oframe = oframe
self.stopped = False
self.module = module
self.ctx = ctx
class_IDs, scores, bounding_boxs = run(x, module, ctx)
self.class_IDs, self.scores, self.bounding_boxs = convertAsNumpy(
class_IDs, bounding_boxs, scores
)
def numpy_convert_test():
"""
Check that convertAsNumpy is able to convert nd.array to np.array
"""
a = nd.array([1, 2, 3])
b = nd.array([1, 2, 3])
c = nd.array([1, 2, 3])
a_new, b_new, c_new = convertAsNumpy(a, b, c)
assert isinstance(a_new, np.ndarray)
assert isinstance(b_new, np.ndarray)
assert isinstance(c_new, np.ndarray)
print("[3/3] Test numpy_convert Pass")
def inf(self):
while not self.stopped:
class_IDs, scores, bounding_boxs = run(self.x, self.module, self.ctx)
class_IDs, bounding_boxs, scores = convertAsNumpy(
class_IDs, bounding_boxs, scores
)
oframe = draw_plates(
class_IDs, scores, bounding_boxs, self.oframe, self.img, self.alpr
)
cv2.imshow("frame", oframe)
if cv2.waitKey(1) & 0xFF == ord("q"):
self.stopped = True
def mxnet_inf_test(frame):
"""
Test getting alpr object, fetching input image, transorming imgae, making inference using mxnet, and converting result to numpy
"""
alpr = get_alpr("eu", "/srv/openalpr")
ctx = mx.cpu()
model_name = "ssd_512_mobilenet1.0_voc"
net = model_zoo.get_model(model_name, pretrained=True, ctx=ctx)
net.hybridize()
x, img = data.transforms.presets.ssd.load_test(frame, short=480)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = net(x)
class_IDs, bounding_boxs, scores = convertAsNumpy(class_IDs, bounding_boxs,
scores)
def detect(target: str, language: str, camera: str, path: str):
"""
Take feed from camera and detect vehicle (using mxnet),
draw bounding box, and read license plate (based on the language)
Parameters
----------
target: str
The device (CPU or GPU) that mxnet will inference on
language: str
Region of the license plate that OpenALPR will detect
camera: str
Specified camera input to use
path: str
Specified path to OpenALPR folder
"""
if target == "llvm":
ctx = mx.cpu()
elif target == "cuda":
ctx = mx.gpu()
else:
print("Target does not exist")
sys.exit(1)
alpr = get_alpr(language, path)
model_name = "ssd_512_mobilenet1.0_voc"
net = model_zoo.get_model(model_name, pretrained=True, ctx=ctx)
net.hybridize()
print("Starting video stream...")
cap = cv2.VideoCapture("/dev/video" + camera)
if not cap.isOpened():
print("Could not open video device (change video_camera)")
sys.exit(1)
fps = FPS().start()
while True:
ret, frame = cap.read()
oframe = frame
frame = mx.nd.array(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).astype("uint8")
x, img = data.transforms.presets.ssd.transform_test(frame, short=480)
x = x.as_in_context(ctx)
class_IDs, scores, bounding_boxs = net(x)
class_IDs, bounding_boxs, scores = convertAsNumpy(
class_IDs, bounding_boxs, scores
)
oframe = draw_plates(class_IDs, scores, bounding_boxs, oframe, img, alpr)
cv2.imshow("frame", oframe)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
fps.update()
fps.stop()
print("Elapsed time: {:.2f}".format(fps.elapsed()))
print("Approx. FPS: {:.2f}".format(fps.fps()))
# clean up capture window
cap.release()
cv2.destroyAllWindows()