def generate_images(im_ch, man_ch):
data = pd.read_excel('VeinsCoordsV05.xlsx')
ch = 0
print('Сгенерированные изображения:', end=' ')
for i in range(1, man_ch + 1):
oneman_im_ch = 1
ch += 1
filename = create_filename(i)
draw_img = Drawing()
draw_img.stroke_color = Color("white")
img = WandImage(width=334, height=118, background=Color("black"))
img.save(filename=filename)
for j in func_dict.keys():
vein = data.iloc[j - 2, 7:]
start_coord, finish_coord = data.iloc[j - 2, 1], data.iloc[j - 2,
3]
x, y = prepare_data(vein)
popt, _ = curve_fit(func_dict.get(j), x, y)
for k in range(popt.shape[0]):
a, b = find_random_diap(popt[k], j)
popt[k] = random.uniform(a, b)
if j in vein_width_dict.keys():
brush_width = random.uniform(
vein_width_dict.get(j)[0],
vein_width_dict.get(j)[1])
else:
brush_width = random.uniform(1, 1.5)
create_line_img(start_coord, finish_coord, brush_width, popt, img,
filename)
img.blur(radius=0, sigma=1)
img.crop(46, 25, 321, 90)
img.save(filename=filename)
oneman_vein_name = filename
print(ch, end=' ')
while (oneman_im_ch < im_ch // man_ch) or (
(ch + im_ch % man_ch == im_ch) and (im_ch % man_ch)):
img = WandImage(filename=oneman_vein_name)
x1_crop = random.randint(0, 40)
y1_crop = random.randint(0, 2)
x2_crop = random.randint(265, 275)
y2_crop = random.randint(60, 63)
if random.random() >= 0.4:
alpha = random.randint(-2, 2)
img.rotate(alpha, background=Color('black'))
img.crop(x1_crop, y1_crop, x2_crop, y2_crop)
if random.random() >= 0.5:
rad = random.randint(1, 10)
alpha = random.randint(-45, 45)
img.motion_blur(radius=rad, sigma=8, angle=alpha)
filename = oneman_vein_name.replace(
'.png', '_' + str(oneman_im_ch) + '.png')
img.save(filename=filename)
oneman_im_ch += 1
ch += 1
print(ch, end=' ')
print('')
print('Изображения сгенерированы')
def shadow(self, x, y, radius=2, sigma=4, fresh=False, color="Black"):
if fresh or not hasattr(self, "pre_shadow") or self.pre_shadow is None:
shadow = self.color_overlay(color, fresh=False).clone()
image = Image(width=int(self.template["width"]),
height=int(self.template["height"]))
image.composite(shadow,
int(self["left"]) + x,
int(self["top"]) + y)
image.blur(radius, sigma)
self.pre_shadow = image
return self.pre_shadow
def blur(image: Image, radius: float, sigma: float) -> None:
image.blur(radius=radius, sigma=sigma)
def _blur(img: Image):
img.blur(0, 5)
return img
def blur(img: Image):
resize(img)
img.blur(0, 5)
return img
