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