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import numpy as np
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from PIL import Image
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# img = r'C:\Users\Administrator\Desktop\Upload\d825cc1e-6518-afd5-1b02-ed88a7a72806.jpg'
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# out_img = r'C:\Users\Administrator\Desktop\Upload\d825cc1e-6518-afd5-1b02-ed88a7a72806_xiangao1.jpg'
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img = r'C:\Users\Administrator\Desktop\Demo\demo3_in_out.png'
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out_img=r'C:\Users\Administrator\Desktop\Demo\demo3_in_out_f.png'
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array = np.asarray(Image.open(img).convert('L')).astype(np.float64)
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# 根据灰度变化来模拟人类视觉的明暗程度
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depth = 10 # 预设虚拟深度值为10 范围为0-100
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# 提取x y方向梯度值 解构赋给grad_x, grad_y
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grad_x, grad_y = np.gradient(array)
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# 利用像素之间的梯度值和虚拟深度值对图像进行重构
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grad_x = grad_x * depth / 100
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grad_y = grad_y * depth / 100
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# 梯度归一化 定义z深度为1. 将三个梯度绝对值转化为相对值，在三维中是相对于斜对角线A的值
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dis = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.0)
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uni_x = grad_x / dis
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uni_y = grad_y / dis
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uni_z = 1.0 / dis
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# 光源俯视角度和光源方位角度
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vec_el = np.pi / 2.2
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vec_az = np.pi / 4
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# 光源对x、y、z轴的影响
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dx = np.cos(vec_el) * np.cos(vec_az)
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dy = np.cos(vec_el) * np.sin(vec_az)
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dz = np.sin(vec_el)
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# 光源归一化
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out = 255 * (uni_x * dx + uni_y * dy + uni_z * dz)
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out = out.clip(0, 255)
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img = Image.fromarray(out.astype(np.uint8))
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img.save(out_img)
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