a = np.asarray(Image.open("Annie1.jpg").convert('L')).astype('float')

depth = 10.  # (0-100)
grad = np.gradient(a)  # 取圖像灰度的梯度值
grad_x, grad_y = grad  # 分別取橫縱圖像梯度值
grad_x = grad_x * depth / 100.
grad_y = grad_y * depth / 100.

A = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.)
uni_x = grad_x / A
uni_y = grad_y / A
uni_z = 1. / A

vec_el = np.pi / 2.2  # 光源的俯視角度,弧度值
vec_az = np.pi / 4.  # 光源的方位角度,弧度值
dx = np.cos(vec_el) * np.cos(vec_az)  # 光源對 x軸的影響
dy = np.cos(vec_el) * np.sin(vec_az)  # 光源對 y軸的影響
dz = np.sin(vec_el)  # 光源對z 軸的影響
b = 255 * (dx * uni_x + dy * uni_y + dz * uni_z)  # 光源歸一化,8 255
b = b.clip(0, 255)# 對像素值低于0，高于255部分做截斷處理

im.save("Annie_shouhui.jpg")

from PIL import Image
import numpy as np
a = np.asarray(Image.open("Annie1.jpg").convert('L')).astype('float')
depth = 10.  # (0-100)
grad = np.gradient(a)  # 取圖像灰度的梯度值
grad_x, grad_y = grad  # 分別取橫縱圖像梯度值
grad_x = grad_x * depth / 100.
grad_y = grad_y * depth / 100.
A = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.)
uni_x = grad_x / A
uni_y = grad_y / A
uni_z = 1. / A
vec_el = np.pi / 2.2  # 光源的俯視角度,弧度值
vec_az = np.pi / 4.  # 光源的方位角度,弧度值
dx = np.cos(vec_el) * np.cos(vec_az)  # 光源對 x軸的影響
dy = np.cos(vec_el) * np.sin(vec_az)  # 光源對 y軸的影響
dz = np.sin(vec_el)  # 光源對z 軸的影響
b = 255 * (dx * uni_x + dy * uni_y + dz * uni_z)  # 光源歸一化
b = b.clip(0, 255)
im = Image.fromarray(b.astype('uint8'))  # 重構圖像
im.save("Annie_shouhui.jpg")

you-get --format=dash-flv -o ./ https://www.bilibili.com/video/BV1tT4y1j7a9?from=search&8014393453748720686

 vc = cv2.VideoCapture(video_path)
    c = 0
    if vc.isOpened():
        rval,frame = vc.read()
        height,width = frame.shape[0],frame.shape[1]
        print(height, width)
    else:
        rval = False
        height,width = 960,1200
    # jpg_list = [os.path.join('Pic_Directory/',i) for i in os.listdir('Pic_Directory') if i.endswith('.jpg')]
    fps = 24 # 視頻幀率
    video_path1 = './text.mp4'
    video_writer = cv2.VideoWriter(video_path1,cv2.VideoWriter_fourcc(*'mp4v'),fps,(width,height))
    while rval:
        rval,frame = vc.read()# 讀取視頻幀
        img = coonvert_jpg(Image.fromarray(frame))
        frame_converted = np.array(img)
        # 轉化為三通道
        image = np.expand_dims(frame_converted,axis = 2)
        result_arr = np.concatenate((image,image,image),axis = -1)
        video_writer.write(result_arr)
        print('Sucessfully Conveted---------{}'.format(c))
        c = c + 1
        if c >= 3000:
            break
    video_writer.release()

 # 轉化為三通道
 image = np.expand_dims(frame_converted,axis = 2)
 result_arr = np.concatenate((image,image,image),axis = -1)