def get_sensor_last_image(id):
processor = ImageProcessor()
print(f'requesting last image with id={id}')
scaled_up = request.args.get('scale_up')
scaled_up = 1 if scaled_up is None else int(scaled_up)
interpolate = request.args.get('interpolate')
interpolate = True if interpolate == "1" else False
simulated = request.args.get('simulate')
simulated = True if simulated == "1" else False
if simulated:
last_result = Measurement_test.query.filter(
Measurement_test.sensor_id == id).order_by(
Measurement_test.timestamp.desc()).first()
else:
last_result = Measurement.query.filter(
Measurement.sensor_id == int(id)).order_by(
Measurement.timestamp.desc()).first()
#get processed frame
processor.process(last_result.data)
cv2_data = processor.plot_frame()
img = fast_thermal_image(cv2_data, scale=scaled_up)
return Response(img)
def get_last_thermal_image():
scaled_up = request.args.get('scale_up')
scaled_up = 1 if scaled_up is None else int(scaled_up)
interpolate = request.args.get('interpolate')
interpolate = True if interpolate == "1" else False
simulated = request.args.get('simulate')
simulated = True if simulated == "1" else False
print(scaled_up, interpolate)
if simulated:
last_result = Measurement_test.query.order_by(
Measurement_test.timestamp.desc()).first()
else:
last_result = Measurement.query.order_by(
Measurement.timestamp.desc()).first()
img = fast_thermal_image(32, 24, last_result.data)
print(f'Retrieved image from: {last_result.timestamp}')
return serve_pil_image(img)
def _get_smooth_img(self):
img = self.get_scaled(self.smooth_data, self.scale_imgs)
return fast_thermal_image(img, deltas=self.deltas, dim=img.shape)
FPS = 20
amount_sensors = len(sensor_ids)
amount_extra_webcams = 1
start_index = 424
frame_time_increase = timedelta(milliseconds=1000/FPS)
cur_time = start_time + start_index * frame_time_increase
for extra_time in range(start_index, time_diff * FPS):
print(extra_time)
cur_time += frame_time_increase
cur_indices = [find_closest_time(meas, cur_time, cur_index) for meas, cur_index in zip(measurements, cur_indices)]
raw_imgs = [fast_thermal_image(meas[index].data, smooth=False, side=False) for meas, index in zip(measurements, cur_indices)]
smooth_imgs = [fast_thermal_image(meas[index].data, smooth=True, side=False) for meas, index in zip(measurements, cur_indices)]
proc_imgs = [processed_color_plot(meas[index].data, mtplotlib=False, to_pil=True) for meas, index in zip(measurements, cur_indices)]
webcam_imgs = [get_webcam_img(meas[index]) for meas, index in zip(measurements, cur_indices)]
extra_webcams = [get_webcam_img(meas[index], sensor_id=-1) for meas, index in zip(measurements, cur_indices)]
extra_webcam = None
rescaled_webcam = [img.resize((320, 180)) if img is not None else None for img in webcam_imgs]
for webcam in extra_webcams:
if webcam is not None:
extra_webcam = webcam
break
img_width = 1244 + amount_sensors * 340
def get_scaled_img(self):
img = self.get_scaled(self.scaled_data, self.scale_imgs)
return fast_thermal_image(img, dim=img.shape)