discretize.py

def create_img(xy, elements, background, element_id, thetas, width=256, height=256, scale=1.0, gen_img=None, color=None, background_img=None):
    scale= int(scale)
    canvas_size = [width*scale, height*scale]
    xy = xy*scale
    for i in range(len(elements)):
        w_e = elements[i].size[0]
        h_e = elements[i].size[1]
        elements[i] = elements[i].resize((w_e*scale, h_e*scale), resample=PIL.Image.BICUBIC)
    namespace_map = {0:'A', 1:'B', 2:'C', 3:'D', 4:'E', 5:'F', 6:'G', 7:'H'}
    alpha_mask = 0
    if gen_img == None:
        gen_img = Image.new('RGBA', tuple(canvas_size),
                                (background[0], background[1], background[2], alpha_mask))  # create new white image and paste source image into the center
        empty = Image.new('RGBA', tuple(canvas_size),
                                (0, 0, 0, 0))
    total_number = len(element_id)
    for idx in range(len(element_id)):
        (x, y), i, angel = xy[idx], element_id[idx], thetas[idx]
        x = math.ceil(x*width)
        y = math.ceil(y*height)
        element = elements[i].rotate(-angel*3.14159 * 57.2958, resample=Image.BILINEAR)
        if type(color)!=type(None):
            image = to_numpy(element)
            # image[:,:,:3]=1
            image[:, :, :3] = image[:, :, :3]*color[None, idx, :]
            element = to_pil(image)
        w_2 = math.ceil(element.size[0]/2)
        h_2 = math.ceil(element.size[1]/2)
        empty.paste(element, box=(x- w_2, y- h_2, x+w_2 , y+h_2 ))
        print('{:02d}-{}'.format(total_number-idx, namespace_map[i]), x/(2*width), y/(2*height))
        empty.save(folder_final + '{:02d}-{}'.format(total_number-idx, namespace_map[i]) + '.png')
        gen_img = Image.alpha_composite(gen_img, empty)
        empty = Image.new('RGBA', tuple(canvas_size),
                          (0, 0, 0, 0))
    if type(background_img)==type(None):
        bg = Image.new('RGBA', tuple(canvas_size),
                            (background[0], background[1], background[2],
                             255))  # create new white image and paste source image into the center
    else:
        bg = background_img
    img_w_bg = Image.alpha_composite(bg, gen_img)
    return gen_img, img_w_bg

def calculate_color(c_variables, background):
    z = torch.exp(c_variables[6] / 25)
    z_hat = z / (z + 2000)
    bg_hat = 2000 / (z + 2000)
    # color_value_sig = torch.clamp(c_variables[8] / 20, min=0, max=1)#torch.nn.functional.sigmoid(c_variables[8])
    color_scaled = c_variables[8] / 20
    color_value_sig = torch.max(torch.min(color_scaled, 1 + (color_scaled - 1) * 0.001), 0.001 * color_scaled)
    # color_values = (color_value_sig).to("cpu", torch.float).data.numpy()
    color_values = ((color_value_sig * z_hat[:, None] + bg_hat[:, None] * background[:, :, 0, 0])).to("cpu",
                                                                                                      torch.float).data.numpy()
    return color_values