def infer():
# Image source
cam = Camera()
stream = cam.get_stream()
floorNet = FloorNet()
# Prediction
while True:
start = time.time()
print("======================================")
# Infer
raw_img = stream.get()
img, mask = floorNet.predict(raw_img)
# Visualize
mask = cv.cvtColor(mask, cv.COLOR_GRAY2BGR)
cv.addWeighted(mask, 0.5, img, 0.5, 0, img)
cv.imshow('Camera', img)
# Calculate frames per second (FPS)
end = time.time()
print('Total estimated time: {:.4f}'.format(end-start))
fps = 1/(end-start)
print("FPS: {:.1f}".format(fps))
if cv.waitKey(10) & 0xFF == ord('q'):
break
# Clear resources
cv.destroyAllWindows()
cam.terminate()
def infer():
# Image source
cam = Camera()
stream = cam.get_stream()
# Load edge model
EDGE_MODEL = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'../models/tpu/ohmnilabs_floornet_224_quant_postprocess_edgetpu.tflite')
inference = Inference(EDGE_MODEL)
# Prediction
while True:
start = time.time()
print("======================================")
# Infer
raw_img = stream.get()
img, mask = inference.predict(raw_img)
# Visualize
mask = cv.cvtColor(mask, cv.COLOR_GRAY2BGR)
cv.addWeighted(mask, 0.5, img, 0.5, 0, img)
cv.imshow('Camera', img)
# Calculate frames per second (FPS)
end = time.time()
print('Total estimated time: {:.4f}'.format(end-start))
fps = 1/(end-start)
print("FPS: {:.1f}".format(fps))
if cv.waitKey(10) & 0xFF == ord('q'):
break
# Clear resources
cv.destroyAllWindows()
cam.terminate()
def predict():
# Config
image_shape = (224, 224)
output_shape = (640, 480)
alpha = 0.5
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
out = cv.VideoWriter(
'dist/floorNet-%s.avi' % current_time, cv.VideoWriter_fourcc(*'DIVX'), 10, output_shape)
# Model
detector = Detector(image_shape, 'models')
# Image source
cam = Camera()
stream = cam.get_stream()
# Prediction
while True:
start = time.time()
print("======================================")
img = stream.get()
img = detector.normalize(img)
mask = detector.predict(img)
mask = cv.cvtColor(mask, cv.COLOR_GRAY2BGR)
cv.addWeighted(mask, alpha, img, 1-alpha, 0, img)
img = cv.resize(img, output_shape)
cv.imshow('Camera', img)
# Save video
frame = (img*255).astype(np.uint8)
out.write(frame)
# Calculate frames per second (FPS)
end = time.time()
print('Total estimated time: {:.4f}'.format(end-start))
fps = 1/(end-start)
print("FPS: {:.1f}".format(fps))
if cv.waitKey(10) & 0xFF == ord('q'):
break
# Clear resources
out.release()
cv.destroyAllWindows()
cam.terminate()
def test(self):
cam = Camera()
stream = cam.get_stream()
while True:
timer = cv.getTickCount()
print("===========================")
img = stream.get()
cv_img = cv.resize(img, self.pose.input_shape)
pil_img = image.convert_cv_to_pil(cv_img)
activated, obj_img, bbox, objects, inference_time = self.pose.predict(
cv_img)
print('Inference time: {:.4f}'.format(inference_time / 1000))
drawed_img = ImageDraw.Draw(pil_img)
for marks in objects:
for mark in marks:
(label, score, x, y) = mark
if label in LABEL_FILTER:
self.draw_pose(drawed_img, x, y, label, score)
# Calculate frames per second (FPS)
print('Total Estimated Time: {:.4f}'.format(
(cv.getTickCount() - timer) / cv.getTickFrequency()))
fps = cv.getTickFrequency() / (cv.getTickCount() - timer)
print('FPS: {:.1f}'.format(fps))
print('\n')
if activated:
self.draw_text(drawed_img, 'Activated')
cv.imshow('Activation', obj_img)
cv.moveWindow('Activation', 90, 650)
else:
self.draw_text(drawed_img, 'Idle')
cv.imshow('Video', image.convert_pil_to_cv(pil_img))
if cv.waitKey(10) & 0xFF == ord('q'):
break
cv.destroyWindow('Activation')
cv.destroyWindow('Video')
cam.terminate()
