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import cv2.detail
import numpy as np
def judge_layout(input_width, input_height, PHOTO_INTERVAL_W, PHOTO_INTERVAL_H, LIMIT_BLOCK_W, LIMIT_BLOCK_H):
centerBlockHeight_1, centerBlockWidth_1 = input_height, input_width # 由证件照们组成的一个中心区块（1代表不转置排列）
centerBlockHeight_2, centerBlockWidth_2 = input_width, input_height # 由证件照们组成的一个中心区块（2代表转置排列）
# 1.不转置排列的情况下：
layout_col_no_transpose = 0 # 行
layout_row_no_transpose = 0 # 列
for i in range(1, 4):
centerBlockHeight_temp = input_height * i + PHOTO_INTERVAL_H * (i-1)
if centerBlockHeight_temp < LIMIT_BLOCK_H:
centerBlockHeight_1 = centerBlockHeight_temp
layout_row_no_transpose = i
else:
break
for j in range(1, 9):
centerBlockWidth_temp = input_width * j + PHOTO_INTERVAL_W * (j-1)
if centerBlockWidth_temp < LIMIT_BLOCK_W:
centerBlockWidth_1 = centerBlockWidth_temp
layout_col_no_transpose = j
else:
break
layout_number_no_transpose = layout_row_no_transpose*layout_col_no_transpose
# 2.转置排列的情况下：
layout_col_transpose = 0 # 行
layout_row_transpose = 0 # 列
for i in range(1, 4):
centerBlockHeight_temp = input_width * i + PHOTO_INTERVAL_H * (i-1)
if centerBlockHeight_temp < LIMIT_BLOCK_H:
centerBlockHeight_2 = centerBlockHeight_temp
layout_row_transpose = i
else:
break
for j in range(1, 9):
centerBlockWidth_temp = input_height * j + PHOTO_INTERVAL_W * (j-1)
if centerBlockWidth_temp < LIMIT_BLOCK_W:
centerBlockWidth_2 = centerBlockWidth_temp
layout_col_transpose = j
else:
break
layout_number_transpose = layout_row_transpose*layout_col_transpose
if layout_number_transpose > layout_number_no_transpose:
layout_mode = (layout_col_transpose, layout_row_transpose, 2)
return layout_mode, centerBlockWidth_2, centerBlockHeight_2
else:
layout_mode = (layout_col_no_transpose, layout_row_no_transpose, 1)
return layout_mode, centerBlockWidth_1, centerBlockHeight_1
def generate_layout_photo(input_height, input_width):
# 1.基础参数表
LAYOUT_WIDTH = 1746
LAYOUT_HEIGHT = 1180
PHOTO_INTERVAL_H = 30 # 证件照与证件照之间的垂直距离
PHOTO_INTERVAL_W = 30 # 证件照与证件照之间的水平距离
SIDES_INTERVAL_H = 50 # 证件照与画布边缘的垂直距离
SIDES_INTERVAL_W = 70 # 证件照与画布边缘的水平距离
LIMIT_BLOCK_W = LAYOUT_WIDTH - 2*SIDES_INTERVAL_W
LIMIT_BLOCK_H = LAYOUT_HEIGHT - 2*SIDES_INTERVAL_H
# 2.创建一个1180x1746的空白画布
white_background = np.zeros([LAYOUT_HEIGHT, LAYOUT_WIDTH, 3], np.uint8)
white_background.fill(255)
# 3.计算照片的layout（列、行、横竖朝向）,证件照组成的中心区块的分辨率
layout_mode, centerBlockWidth, centerBlockHeight = judge_layout(input_width, input_height, PHOTO_INTERVAL_W,
PHOTO_INTERVAL_H, LIMIT_BLOCK_W, LIMIT_BLOCK_H)
# 4.开始排列组合
x11 = (LAYOUT_WIDTH - centerBlockWidth)//2
y11 = (LAYOUT_HEIGHT - centerBlockHeight)//2
typography_arr = []
typography_rotate = False
if layout_mode[2] == 2:
input_height, input_width = input_width, input_height
typography_rotate = True
for j in range(layout_mode[1]):
for i in range(layout_mode[0]):
xi = x11 + i*input_width + i*PHOTO_INTERVAL_W
yi = y11 + j*input_height + j*PHOTO_INTERVAL_H
typography_arr.append([xi, yi])
return typography_arr, typography_rotate
def generate_layout_image(input_image, typography_arr, typography_rotate, width=295, height=413):
LAYOUT_WIDTH = 1746
LAYOUT_HEIGHT = 1180
white_background = np.zeros([LAYOUT_HEIGHT, LAYOUT_WIDTH, 3], np.uint8)
white_background.fill(255)
if input_image.shape[0] != height:
input_image = cv2.resize(input_image, (width, height))
if typography_rotate:
input_image = cv2.transpose(input_image)
height, width = width, height
for arr in typography_arr:
locate_x, locate_y = arr[0], arr[1]
white_background[locate_y:locate_y+height, locate_x:locate_x+width] = input_image
return white_background
if __name__ == "__main__":
typography_arr, typography_rotate = generate_layout_photo(input_height=413, input_width=295)
print("typography_arr:", typography_arr)
print("typography_rotate:", typography_rotate)
result_image = generate_layout_image(cv2.imread("./32.jpg"), typography_arr, typography_rotate, width=295, height=413)
cv2.imwrite("./result_image.jpg", result_image)
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