def blend_last_row_multiple_gradient_thread(i, j, x_max, image_np,
valid_bit_np, ens_prediction,
blended_img, gradient):
blended_np = np.zeros((image_np.shape[1], image_np.shape[2], 3),
dtype='uint8')
index = i * x_max + j
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
if max(ens_prediction[2]) - min(ens_prediction[2]) != 0:
var_norm = (ens_prediction[2][index] - min(ens_prediction[2])) / (
max(ens_prediction[2]) - min(ens_prediction[2]))
else:
var_norm = ens_prediction[2][index]
var_mean = ((1 - var_norm) + ens_prediction[0][index]) / 2
class_idx = ens_prediction[0][index]
if valid_bit_np[index] == 1:
color = get_prob_color(gradient[class_idx], ens_prediction[1][index])
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
t_x = j * image_np.shape[2]
t_y = blended_img.size[1] - image_np.shape[1]
blended_img.paste(blend(base_img, mask, 0.5, var_mean), (t_x, t_y))
else:
color = get_prob_color(gradient[2], 1)
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
t_x = j * image_np.shape[2]
t_y = blended_img.size[1] - image_np.shape[1]
blended_img.paste(blend(base_img, mask, 0.5, 1), (t_x, t_y))
def blend_last_std_bigradient(image_np, valid_bit_np, ens_prediction,
blended_img, gradient):
blended_np = np.zeros((image_np.shape[1], image_np.shape[2], 3),
dtype='uint8')
index = -1
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
if max(ens_prediction[2]) - min(ens_prediction[2]) != 0:
var_norm = (ens_prediction[2][index] - min(ens_prediction[2])) / (
max(ens_prediction[2]) - min(ens_prediction[2]))
else:
var_norm = 0
var_mean = 1 - var_norm
class_idx = ens_prediction[0][index]
if valid_bit_np[index] == 1:
color = get_prob_color_bigradient(gradient[class_idx],
ens_prediction[1][index])
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
t_x = blended_img.size[0] - image_np.shape[2]
t_y = blended_img.size[1] - image_np.shape[1]
blended_img.paste(blend(base_img, mask, 0.5, var_mean), (t_x, t_y))
else:
color = get_prob_color_bigradient(gradient[2], 1)
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
t_x = blended_img.size[0] - image_np.shape[2]
t_y = blended_img.size[1] - image_np.shape[1]
blended_img.paste(blend(base_img, mask, 0.5, 1), (t_x, t_y))
def blend_last_column_thread(i, x_max, image_np, valid_bit_np, ens_prediction,
blended_img):
blended_np = np.zeros((image_np.shape[1], image_np.shape[2], 3),
dtype='uint8')
index = i * x_max + x_max - 1
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
var_norm = ens_prediction[2][index]
if max(ens_prediction[2]) - min(ens_prediction[2]) != 0:
var_norm = (ens_prediction[2][index] - min(ens_prediction[2])) / (
max(ens_prediction[2]) - min(ens_prediction[2]))
var_mean = 1 - var_norm
if valid_bit_np[index] == 1:
color = get_class_color(ens_prediction[0][index])
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
t_x = blended_img.size[0] - image_np.shape[2]
t_y = i * image_np.shape[1]
blended_img.paste(blend(base_img, mask, 0.5, var_mean), (t_x, t_y))
else:
color = get_class_color(2)
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
t_x = blended_img.size[0] - image_np.shape[2]
t_y = i * image_np.shape[1]
blended_img.paste(blend(base_img, mask, 0.5, 1), (t_x, t_y))
def blend_thread(i, j, x_max, image_np, valid_bit_np, ens_prediction,
blended_img):
blended_np = np.zeros((image_np.shape[1], image_np.shape[2], 3),
dtype='uint8')
helvetica = ImageFont.truetype(font=font_, size=20)
text_color = (0, 0, 0)
d = ImageDraw.Draw(blended_img)
index = i * x_max + j
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
var_norm = ens_prediction[2][index]
if max(ens_prediction[2]) - min(ens_prediction[2]) != 0:
var_norm = (ens_prediction[2][index] - min(ens_prediction[2])) / (
max(ens_prediction[2]) - min(ens_prediction[2]))
var_mean = 1 - var_norm
if valid_bit_np[index] == 1:
color = get_class_color(ens_prediction[0][index])
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
blended_img.paste(blend(base_img, mask, 0.5, var_mean),
(j * image_np.shape[2], i * image_np.shape[1]))
k = 10
location = (j * image_np.shape[2] + 10, i * image_np.shape[1] + k)
d.text(location,
CATEGORIES[ens_prediction[0][index]] +
" mean: {:.2}".format(ens_prediction[1][index]),
font=helvetica,
fill=text_color)
k = k + 30
if ens_prediction[0][index] != 2:
location = (j * image_np.shape[2] + 10, i * image_np.shape[1] + k)
d.text(location,
"H mean: {:.2}".format(ens_prediction[3][index]),
font=helvetica,
fill=text_color)
k = k + 30
if PRINT_STD:
location = (j * image_np.shape[2] + 10, i * image_np.shape[1] + k)
d.text(location,
"Std: {:.2%}".format(ens_prediction[2][index]),
font=helvetica,
fill=text_color)
k = k + 30
if PRINT_STD:
location = (j * image_np.shape[2] + 10, i * image_np.shape[1] + k)
d.text(location,
"Norm std: {:.2%}".format(var_norm),
font=helvetica,
fill=text_color)
else:
color = get_class_color(2)
blended_np[:, :, 0].fill(color[0])
blended_np[:, :, 1].fill(color[1])
blended_np[:, :, 2].fill(color[2])
mask = np_to_pil(blended_np, COLOR)
blended_img.paste(blend(base_img, mask, 0.5, 1),
(j * image_np.shape[2], i * image_np.shape[1]))
def print_std_bigradient():
image_np = np.zeros((30, 414, 3), "uint8")
gradient = get_color_std_bigradient()
lst = gradient[2]
lst.reverse()
for i in range(0, 30):
for j in range(0, 207):
image_np[i][j][0] = lst[math.floor(j / 3)][0]
image_np[i][j][1] = lst[math.floor(j / 3)][1]
image_np[i][j][2] = lst[math.floor(j / 3)][2]
for i in range(0, 30):
for j in range(207, 414):
idx = math.floor((j - 207) / 3)
x = gradient[0][idx][0]
y = gradient[0][idx][1]
z = gradient[0][idx][2]
image_np[i][j][0] = x
image_np[i][j][1] = y
image_np[i][j][2] = z
image = np_to_pil(image_np, COLOR)
save_image(image, RESOURCE_FOLDER, "std_bigradient")
def save_last_np(image_np, blended_img):
index = -1
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
t_x = blended_img.size[0] - image_np.shape[2]
t_y = blended_img.size[1] - image_np.shape[1]
blended_img.paste(base_img, (t_x, t_y))
def save_last_row_np_thread(i, j, x_max, image_np, blended_img):
index = i * x_max + j
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
t_x = j * image_np.shape[2]
t_y = blended_img.size[1] - image_np.shape[1]
blended_img.paste(base_img, (t_x, t_y))
def save_last_column_np_thread(i, x_max, image_np, blended_img):
index = i * x_max + x_max - 1
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
t_x = blended_img.size[0] - image_np.shape[2]
t_y = i * image_np.shape[1]
blended_img.paste(base_img, (t_x, t_y))
def print_std_gradient():
gradient = get_color_std_gradient()
image = np_to_pil(gradient, COLOR)
save_image(image, RESOURCE_FOLDER, "std_gradient")
def save_np_thread(i, j, x_max, image_np, blended_img):
index = i * x_max + j
base_img = np_to_pil(image_np[index][:][:][:], COLOR)
blended_img.paste(base_img, (j * image_np.shape[2], i * image_np.shape[1]))
