def get_background(self):
pallete_object = Pallete_generation()
pallete_image = pallete_object.get_pallete_image(self.color_legend_width, self.shown_image_height)
total_width = self.color_legend_width + self.spacing_width + self.shown_image_width
white_background_object = White_background()
white_background_image = white_background_object.get_black_background(total_width, self.shown_image_height)
combined_background = self.combine_background(pallete_image,white_background_image)
return combined_background
def get_background(self):
pallete_object = Pallete_generation()
pallete_image = pallete_object.get_pallete_image(
self.color_legend_width, self.shown_image_height)
total_width = self.color_legend_width + self.spacing_width + self.shown_image_width
white_background_object = White_background()
white_background_image = white_background_object.get_black_background(
total_width, self.shown_image_height)
combined_background = self.combine_background(pallete_image,
white_background_image)
return combined_background
class display_interaction():
def __init__(self):
self.pallete_object = Pallete_generation()
self.pallete_numpy = self.pallete_object.get_pallete_numpy_array()
# Colours the image according to pallete and values
def colour_thermal_image(self, thermal_image_8bit):
# Colour the image
thermal_image_coloured = self.pallete_numpy[thermal_image_8bit]
# Convert to RGB from BGR - takes ~0.01s
thermal_image_coloured = cv2.cvtColor(thermal_image_coloured,
cv2.COLOR_BGR2RGB)
return thermal_image_coloured
# Produces coloured, SCALLED image from raw data TODO: check performance other way around
def coloured_image_from_raw(self, raw_values, image_scalar):
STORED_IMAGE_NAME = "DetectionImage1.jpg"
lepton_object = Lepton_interface()
# Convert raw data to 8 bit image - takes 0.0008s
bit8_image = lepton_object.raw_to_8bit(raw_values)
bit8_image = bit8_image.astype(uint8)
# Remove non-needed dimension
bit8_image = bit8_image.squeeze()
# Colour image takes ~0.005
coloured_image = self.colour_thermal_image(bit8_image)
# Scale 8 bit coloured image # CV_INTER_CUBIC might be used - slow but looks better. INTER_NEAREST.
# INTER_LINEAR takes ~0.025s INTER_CUBIC - ~0.07s. Nearest - 0.01s
coloured_image = cv2.resize(coloured_image, (0, 0), None, image_scalar,
image_scalar, cv2.INTER_LINEAR)
# Takes ~0.2s, updated one takes ~0.0001s
# cv2.imwrite(STORED_IMAGE_NAME, np.uint8(coloured_image)) # Write the image to file
mat_array = cv.fromarray(coloured_image)
# Take the image from file as SIMPLECV image. Updated one ~0.005s
simplecv_img = Image(mat_array)
# simplecv_img = Image(STORED_IMAGE_NAME)
return simplecv_img
# Combines acquired image, background image and legend layer
def combine_image(self, coloured_scaled, legend_layer, background_image,
hot_spots_layer):
IMAGE_X_OFFSET = (background_image.height - coloured_scaled.height) / 2
# Add max temperature circles to coloured image - takes 0.00015s
# Add coloured image to background image - takes 0.02s
combined_image = background_image.blit(coloured_scaled,
pos=(0, IMAGE_X_OFFSET))
# Add legend layer and render it - takes 8*10^-5s
combined_image.addDrawingLayer(legend_layer)
combined_image.addDrawingLayer(hot_spots_layer)
return combined_image
class display_interaction():
def __init__(self):
self.pallete_object = Pallete_generation()
self.pallete_numpy = self.pallete_object.get_pallete_numpy_array()
# Colours the image according to pallete and values
def colour_thermal_image(self, thermal_image_8bit):
# Colour the image
thermal_image_coloured = self.pallete_numpy[thermal_image_8bit]
# Convert to RGB from BGR - takes ~0.01s
thermal_image_coloured = cv2.cvtColor(thermal_image_coloured, cv2.COLOR_BGR2RGB)
return thermal_image_coloured
# Produces coloured, SCALLED image from raw data TODO: check performance other way around
def coloured_image_from_raw(self,raw_values, image_scalar):
STORED_IMAGE_NAME = "DetectionImage1.jpg"
lepton_object = Lepton_interface()
# Convert raw data to 8 bit image - takes 0.0008s
bit8_image = lepton_object.raw_to_8bit(raw_values)
bit8_image = bit8_image.astype(uint8)
# Remove non-needed dimension
bit8_image = bit8_image.squeeze()
# Colour image takes ~0.005
coloured_image = self.colour_thermal_image(bit8_image)
# Scale 8 bit coloured image # CV_INTER_CUBIC might be used - slow but looks better. INTER_NEAREST.
# INTER_LINEAR takes ~0.025s INTER_CUBIC - ~0.07s. Nearest - 0.01s
coloured_image = cv2.resize(coloured_image, (0,0), None, image_scalar, image_scalar, cv2.INTER_LINEAR)
# Takes ~0.2s, updated one takes ~0.0001s
# cv2.imwrite(STORED_IMAGE_NAME, np.uint8(coloured_image)) # Write the image to file
mat_array = cv.fromarray(coloured_image)
# Take the image from file as SIMPLECV image. Updated one ~0.005s
simplecv_img = Image(mat_array)
# simplecv_img = Image(STORED_IMAGE_NAME)
return simplecv_img
# Combines acquired image, background image and legend layer
def combine_image(self, coloured_scaled, legend_layer, background_image, hot_spots_layer):
IMAGE_X_OFFSET = (background_image.height - coloured_scaled.height)/2
# Add max temperature circles to coloured image - takes 0.00015s
# Add coloured image to background image - takes 0.02s
combined_image = background_image.blit(coloured_scaled, pos=(0,IMAGE_X_OFFSET))
# Add legend layer and render it - takes 8*10^-5s
combined_image.addDrawingLayer(legend_layer)
combined_image.addDrawingLayer(hot_spots_layer)
return combined_image
def __init__(self):
self.pallete_object = Pallete_generation()
self.pallete_numpy = self.pallete_object.get_pallete_numpy_array()
def __init__(self):
self.pallete_object = Pallete_generation()
self.pallete_numpy = self.pallete_object.get_pallete_numpy_array()
