185 lines
7.0 KiB
Python
185 lines
7.0 KiB
Python
"""
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这部分输入face5点位置和原图
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输出aligned cropped face
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"""
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import numpy as np
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import cv2
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import time
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import os
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# image_data: src image
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# image_width, image_height,image_channels: width and height, channels of src image
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# src_x, src_y: 输出image每个像素对应的src image 中的像素位置.
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def sampling(image_data, image_width, image_height, image_channels, src_x, src_y):
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ux = np.floor(src_x).astype(int)
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uy = np.floor(src_y).astype(int)
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# 创建一个与src_x形状相同的空数组，用于存储最终的像素值
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pixel = np.zeros((*src_x.shape, image_channels), dtype=np.uint8)
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# 创建一个掩码数组，标记有效的采样点
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valid_mask = (ux >= 0) & (ux < image_height - 1) & (uy >= 0) & (uy < image_width - 1)
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# 计算插值
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x = src_x - ux
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y = src_y - uy
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# 提取图像数据的各个通道
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image_data_reshape = image_data.reshape(-1, image_channels) # (height * width, channels)
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ux_uy = ux * image_width + uy # (height * width)
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ux_uy_next = ux_uy + 1
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ux_next = (ux + 1) * image_width + uy
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ux_next_next = ux_next + 1
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ux_uy[~valid_mask] = 0
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ux_uy_next[~valid_mask] = 0
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ux_next[~valid_mask] = 0
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ux_next_next[~valid_mask] = 0
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# 使用广播计算各个通道的插值
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top_left = image_data_reshape[ux_uy]
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top_right = image_data_reshape[ux_uy_next]
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bottom_left = image_data_reshape[ux_next]
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bottom_right = image_data_reshape[ux_next_next]
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# 计算插值
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interpolated_top = (1 - y[:, :, np.newaxis]) * top_left + y[:, :, np.newaxis] * top_right
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interpolated_bottom = (1 - y[:, :, np.newaxis]) * bottom_left + y[:, :, np.newaxis] * bottom_right
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interpolated_pixel = (1 - x[:, :, np.newaxis]) * interpolated_top + x[:, :, np.newaxis] * interpolated_bottom
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# 填充最终的像素值
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pixel[valid_mask] = np.clip(interpolated_pixel[valid_mask], 0, 255).astype(np.uint8)
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return pixel
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def spatial_transform(image_data, image_width, image_height, image_channels,
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crop_data, crop_width, crop_height, transformation,
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pad_top=0, pad_bottom=0, pad_left=0, pad_right=0,
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type='LINEAR', dtype='ZERO_PADDING', N=1):
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channels = image_channels
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dst_h = crop_height + pad_top + pad_bottom
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dst_w = crop_width + pad_left + pad_right
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for n in range(N):
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theta_data = transformation.reshape(-1)
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scale = np.sqrt(theta_data[0] ** 2 + theta_data[3] ** 2)
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bx, by = np.meshgrid(np.arange(dst_w) - pad_left, np.arange(dst_h) - pad_top)
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bx = bx.T
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by = by.T
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src_y = theta_data[0] * by + theta_data[1] * bx + theta_data[2]
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src_x = theta_data[3] * by + theta_data[4] * bx + theta_data[5]
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crop_data[:] = sampling(image_data, image_width, image_height, image_channels, src_x, src_y,)
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return True
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def transformation_maker(crop_width, crop_height, points, mean_shape, mean_shape_width, mean_shape_height):
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points_num = len(points) # point 个数 5
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std_points = np.zeros((points_num, 2), dtype=np.float32) # 标准点
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# 生成标准点的坐标
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for i in range(points_num):
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std_points[i, 0] = mean_shape[i * 2] * crop_width / mean_shape_width
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std_points[i, 1] = mean_shape[i * 2 + 1] * crop_height / mean_shape_height
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feat_points = np.array(points, dtype=np.float32).reshape(points_num, 2)
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# 初始化
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sum_x = 0.0
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sum_y = 0.0
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sum_u = 0.0
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sum_v = 0.0
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sum_xx_yy = 0.0
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sum_ux_vy = 0.0
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sum_vx_uy = 0.0
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for c in range(points_num):
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sum_x += std_points[c, 0]
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sum_y += std_points[c, 1]
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sum_u += feat_points[c, 0]
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sum_v += feat_points[c, 1]
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sum_xx_yy += std_points[c, 0] ** 2 + std_points[c, 1] ** 2
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sum_ux_vy += std_points[c, 0] * feat_points[c, 0] + std_points[c, 1] * feat_points[c, 1]
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sum_vx_uy += feat_points[c, 1] * std_points[c, 0] - feat_points[c, 0] * std_points[c, 1]
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if sum_xx_yy <= np.finfo(np.float32).eps:
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return False, None
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q = sum_u - sum_x * sum_ux_vy / sum_xx_yy + sum_y * sum_vx_uy / sum_xx_yy
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p = sum_v - sum_y * sum_ux_vy / sum_xx_yy - sum_x * sum_vx_uy / sum_xx_yy
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r = points_num - (sum_x ** 2 + sum_y ** 2) / sum_xx_yy
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if np.abs(r) <= np.finfo(np.float32).eps:
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return False, None
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a = (sum_ux_vy - sum_x * q / r - sum_y * p / r) / sum_xx_yy
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b = (sum_vx_uy + sum_y * q / r - sum_x * p / r) / sum_xx_yy
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c = q / r
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d = p / r
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transformation = np.zeros((2, 3), dtype=np.float64)
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transformation[0, 0] = transformation[1, 1] = a
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transformation[0, 1] = -b
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transformation[1, 0] = b
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transformation[0, 2] = c
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transformation[1, 2] = d
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return True, transformation
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class FaceAlign:
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def __init__(self) -> None:
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self.crop_width, self.crop_height = 256, 256
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self.mean_shape_width, self.mean_shape_height = 256, 256
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self.mean_face = [ # 标准人脸的特征点的位置
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89.3095, 72.9025,
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169.3095, 72.9025,
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127.8949, 127.0441,
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96.8796, 184.8907,
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159.1065, 184.7601
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]
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# landmarks5 = [
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# [268.99814285714285, 166.26619999999997],
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# [342.636625, 164.43359999999998],
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# [311.5448214285714, 221.24419999999998],
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# [272.2709642857143, 243.23539999999997],
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# [344.2730357142857, 241.40279999999996]
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# ]
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def align(self, image, landmarks5): # 原图image landmarks5
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success, transformation = transformation_maker(self.crop_width, self.crop_height, landmarks5, self.mean_face, self.mean_shape_width, self.mean_shape_height)
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if not success:
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print("Failed to compute transformation matrix.")
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img_height, img_width, img_channels = image.shape
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crop_data = np.zeros((self.crop_height, self.crop_width, 3), dtype=np.uint8)
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success = spatial_transform(image, img_width, img_height, img_channels,
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crop_data, self.crop_width, self.crop_height,
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transformation,
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)
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if success:
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if os.path.exists("./images/result1.jpg"):
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cv2.imwrite("./images/result2.jpg", crop_data, [cv2.IMWRITE_JPEG_QUALITY, 100])
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else:
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cv2.imwrite("./images/result1.jpg", crop_data, [cv2.IMWRITE_JPEG_QUALITY, 100])
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else:
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print("error when spatial_transform...")
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return crop_data
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if __name__ == "__main__":
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fa = FaceAlign()
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landmarks5 = [(240.56920098163752, 111.91879640513824),
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(283.7146242409017, 93.30582481805237),
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(268.9820406889578, 129.202270021718),
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(259.51109411985107, 155.79222943184064),
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(296.34255299971073, 137.17925784475477)]
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landmarks5 = [ [ld5[0],ld5[1]] for ld5 in landmarks5]
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image = cv2.imread("/home/bns/seetaface6Python/seetaFace6Python/asserts/1.jpg")
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fa.align(image = image, landmarks5=landmarks5)
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