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fun_img_Resize.py
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fun_img_Resize.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 26 14:41:11 2021
@author: Xcz
"""
# %%
import cv2
import numpy as np
from PIL import Image
from fun_os import Info_img
from fun_global_var import tree_print, init_GLV_DICT
Image.MAX_IMAGE_PIXELS = 10E10 # Image 的 默认参数 无法处理那么大的图片
# %%
def image_Border(src, dst, loc='a', width=3, color=(0, 0, 0, 255)):
'''
src: (str) 需要加边框的图片路径
dst: (str) 加边框的图片保存路径
loc: (str) 边框添加的位置, 默认是'a'(
四周: 'a' or 'all'
上: 't' or 'top'
右: 'r' or 'rigth'
下: 'b' or 'bottom'
左: 'l' or 'left'
)
width: (int) 边框宽度 (默认是3)
color: (int or 3-tuple) 边框颜色 (默认是0, 表示黑色; 也可以设置为三元组表示RGB颜色)
'''
# 读取图片
img_ori = Image.open(src)
w = img_ori.size[0]
h = img_ori.size[1]
# 添加边框
if loc in ['a', 'all']:
w += 2 * width
h += 2 * width
img_new = Image.new('RGBA', (w, h), color)
img_new.paste(img_ori, (width, width))
elif loc in ['t', 'top']:
h += width
img_new = Image.new('RGBA', (w, h), color)
img_new.paste(img_ori, (0, width, w, h))
elif loc in ['r', 'right']:
w += width
img_new = Image.new('RGBA', (w, h), color)
img_new.paste(img_ori, (0, 0, w - width, h))
elif loc in ['b', 'bottom']:
h += width
img_new = Image.new('RGBA', (w, h), color)
img_new.paste(img_ori, (0, 0, w, h - width))
elif loc in ['l', 'left']:
w += width
img_new = Image.new('RGBA', (w, h), color)
img_new.paste(img_ori, (width, 0, w, h))
else:
pass
# 保存图片
img_new.save(dst)
# %%
def image_Add_black_border(img_full_name="Grating.png",
border_percentage=0.5,
is_print=1, **kwargs):
# img_full_name = "Grating.png"
# border_percentage = 0.5 # 边框 占图片的 百分比，也即 图片 放大系数
is_print and print(tree_print(kwargs.get("is_end", 0), kwargs.get("add_level", 0)) + "加黑边")
kwargs.pop("is_end", 0) # 该 def 子分支 后续默认 is_end = 0，如果 kwargs 还会被 继续使用 的话。
# 其实 外面的 kwargs 没传进来，所以这里 直接就是 is_end = 0，add_level = 0
# %%
# 预处理 导入图片 为方形，并加边框
img_name, img_name_extension, img_address, folder_address, img_squared_address, img_squared_bordered_address \
= Info_img(img_full_name)
img = cv2.imdecode(np.fromfile(img_address, dtype=np.uint8), 0) # 按 相对路径 + 灰度图 读取图片
is_print and print(tree_print() + "img.shape = {}".format(img.shape))
if img.shape[0] != img.shape[1]: # 如果 高 ≠ 宽
if img.shape[0] < img.shape[1]: # 如果 图片很宽，就上下加 黑色_不透明_边框
image_Border(img_address, img_squared_address, loc='t', width=(img.shape[1] - img.shape[0]) // 2,
color=(0, 0, 0, 255))
if (img.shape[1] - img.shape[0]) % 2 == 1: # 如果 宽高差 是 奇数，则 下边框 多加一个 像素
image_Border(img_squared_address, img_squared_address, loc='b',
width=(img.shape[1] - img.shape[0]) // 2 + 1, color=(0, 0, 0, 255))
else:
image_Border(img_squared_address, img_squared_address, loc='b',
width=(img.shape[1] - img.shape[0]) // 2, color=(0, 0, 0, 255))
else: # 如果 图片很高，就左右加 黑色_不透明_边框
image_Border(img_address, img_squared_address, loc='l', width=(img.shape[0] - img.shape[1]) // 2,
color=(0, 0, 0, 255))
if (img.shape[0] - img.shape[1]) % 2 == 1: # 如果 高宽差 是 奇数，则 右边框 多加一个 像素
image_Border(img_squared_address, img_squared_address, loc='r',
width=(img.shape[0] - img.shape[1]) // 2 + 1, color=(0, 0, 0, 255))
else:
image_Border(img_squared_address, img_squared_address, loc='r',
width=(img.shape[0] - img.shape[1]) // 2, color=(0, 0, 0, 255))
else:
image_Border(img_address, img_squared_address, loc='a', width=0, color=(0, 0, 0, 255))
img_squared = cv2.imdecode(np.fromfile(img_squared_address, dtype=np.uint8), 0) # 按 绝对路径 + 灰度图 读取图片
is_print and print(tree_print() + "img_squared.shape = {}".format(img_squared.shape))
border_width = int(img_squared.shape[0] * border_percentage / 2)
image_Border(img_squared_address, img_squared_bordered_address, loc='a', width=border_width, color=(0, 0, 0, 255))
img_squared_bordered = cv2.imdecode(np.fromfile(img_squared_bordered_address, dtype=np.uint8),
0) # 按 相对路径 + 灰度图 读取图片
is_print and print(tree_print(kwargs.get("is_end_last", 1)) + "U.shape = img_squared_bordered.shape = {}".format(img_squared_bordered.shape))
# %%
def if_image_Add_black_border(U_name, img_full_name,
is_name_main, is_print, **kwargs, ): # 没有 该函数作为起始的 py 文件，需要加 init_GLV_DICT
if is_name_main: # 等价于：如果是 第一次 进入该程序
# %% 开始 加边框
# print(1, kwargs)
# init_GLV_DICT(**kwargs) # 这里初始化的 init_GLV 传了参数进去 —— 没懂为什么得是 **....，不然传进去变成位置参数 args 中的一元素了
kwargs.pop("is_end", 0)
if ((type(U_name) != str) or U_name == "") and ("U" not in kwargs and "U1" not in kwargs):
border_percentage = kwargs["border_percentage"] if "border_percentage" in kwargs else 0.1
kwargs.pop("border_percentage", None)
is_end_last=-1 if kwargs.get('ray', "2") == "3" else 1
image_Add_black_border(img_full_name, # 预处理 导入图片 为方形，并加边框
border_percentage,
is_print, is_end_last=is_end_last) # 没把 kwargs 传进来，因此 外面的 is_end = 1 不会进来，也就不会 使加黑边 为 末尾
if kwargs.get('ray', "2") == "3":
U2_name, img2_full_name = kwargs.get("U2_name", U_name), kwargs.get("img2_full_name", img_full_name)
if ((type(U2_name) != str) or U2_name == "") and ("U2" not in kwargs):
border_percentage = kwargs["border_percentage"] if "border_percentage" in kwargs else 0.1
kwargs.pop("border_percentage", None)
image_Add_black_border(img2_full_name, # 预处理 导入图片 为方形，并加边框
border_percentage,
is_print, add_level=1) # 没把 kwargs 传进来，因此 外面的 is_end = 1 不会进来，也就不会 使加黑边 为 末尾
# %%
# 需要先将 目标 U_0 = img_squared 给 放大 或 缩小 到 与 全息图（结构） 横向尺寸 Ix_structure, Iy_structure 相同，才能开始 之后的工作
def img_squared_Resize(img_full_name, img_squared,
Ix_structure, Iy_structure, Ix, Iy,
is_print=1, **kwargs):
img_name, img_name_extension, img_address, folder_address, img_squared_address, img_squared_bordered_address \
= Info_img(img_full_name)
is_print and print(tree_print(kwargs.get("is_end", 0), kwargs.get("add_level", 0)) + "图片裁剪")
kwargs.pop("is_end", 0) # 该 def 子分支 后续默认 is_end = 0，如果 kwargs 还会被 继续使用 的话。
img_squared_resize = cv2.resize(img_squared, (Iy_structure, Ix_structure), interpolation=cv2.INTER_AREA)
# 使用cv2.imread()读取图片之后,数据的形状和维度布局是(H,W,C),但是使用函数cv2.resize()进行缩放时候,传入的目标形状是(W,H)
img_squared_resize_full_name = "1.1. " + img_name + "_squared" + "_resize" + img_name_extension
img_squared_resize_address = folder_address + "\\" + img_squared_resize_full_name
# cv2.imwrite(img_squared_resize_address, img_squared_resize) # 保存 img_squared_resize，但不能有 中文路径
cv2.imencode(img_squared_resize_address, img_squared_resize)[1].tofile(img_squared_resize_address)
is_print and print(tree_print() + "img_squared_resize.shape = {}".format(img_squared_resize.shape))
img_squared_resize_bordered_full_name = "2.2. " + img_name + "_squared" + "_resize" + "_bordered" + img_name_extension
img_squared_resize_bordered_address = folder_address + "\\" + img_squared_resize_bordered_full_name
border_width_x = (Ix - Ix_structure) // 2
border_width_y = (Iy - Iy_structure) // 2
# 上下加边框 border_width_x
image_Border(img_squared_resize_address, img_squared_resize_bordered_address, loc='t', width=border_width_x,
color=(0, 0, 0, 255))
image_Border(img_squared_resize_bordered_address, img_squared_resize_bordered_address, loc='b', width=border_width_x,
color=(0, 0, 0, 255))
# 左右加边框 border_width_y
image_Border(img_squared_resize_bordered_address, img_squared_resize_bordered_address, loc='r', width=border_width_y,
color=(0, 0, 0, 255))
image_Border(img_squared_resize_bordered_address, img_squared_resize_bordered_address, loc='l', width=border_width_y,
color=(0, 0, 0, 255))
# 上: 't' or 'top'
# 右: 'r' or 'rigth'
# 下: 'b' or 'bottom'
# 左: 'l' or 'left'
from fun_global_var import Set
Set("border_width_x", border_width_x)
Set("border_width_y", border_width_y)
img_squared_resize_bordered = cv2.imdecode(np.fromfile(img_squared_resize_bordered_address, dtype=np.uint8),
0) # 按 相对路径 + 灰度图 读取图片
is_print and print(tree_print(1) + "structure_squared.shape = img_squared_resize_bordered.shape = {}"
.format(img_squared_resize_bordered.shape))
return border_width_x, border_width_y, img_squared_resize_full_name, img_squared_resize
# %%
def U_resize(U, U_pixels_x, U_pixels_y, Ix, Iy):
if U_pixels_x > 0:
U = U_resize_x(U, U_pixels_x, Iy)
if U_pixels_y > 0:
U = U_resize_y(U, U_pixels_y, Ix)
return U
def U_resize_x(U, U_pixels_x, Iy):
if U_pixels_x > Iy: # 如果 U_pixels_x 比 图片宽，则需要 np.pad 填充零
one_side_pixels_x = (U_pixels_x - Iy) // 2
other_side_pixels_x = (U_pixels_x - Iy) - one_side_pixels_x
U = np.pad(U, ((0, 0), (one_side_pixels_x, other_side_pixels_x)),
'constant', constant_values=(0, 0)) # ((行前, 行后) 填充行, (列前, 列后) 填充列)
elif U_pixels_x < Iy: # 如果 U_pixels_x 比 图片窄，则需要 裁剪图片
one_side_pixels_x = (Iy - U_pixels_x) // 2
other_side_pixels_x = (Iy - U_pixels_x) - one_side_pixels_x
U = U[:, one_side_pixels_x:(-other_side_pixels_x)] # 去掉前 one_side_pixels_x 个，和后 other_side_pixels_x 个 列
return U
def U_resize_y(U, U_pixels_y, Ix):
if U_pixels_y > Ix: # 如果 U_pixels_x 比 图片宽，则需要 np.pad 填充零
one_side_pixels_y = (U_pixels_y - Ix) // 2
other_side_pixels_x = (U_pixels_y - Ix) - one_side_pixels_y
U = np.pad(U, ((one_side_pixels_y, other_side_pixels_x), (0, 0)),
'constant', constant_values=(0, 0)) # ((行前, 行后) 填充行, (列前, 列后) 填充列)
elif U_pixels_y < Ix: # 如果 U_pixels_y 比 图片矮，则需要 裁剪图片
one_side_pixels_y = (Ix - U_pixels_y) // 2
other_side_pixels_x = (Ix - U_pixels_y) - one_side_pixels_y
U = U[one_side_pixels_y:(-other_side_pixels_x), :] # 去掉前 one_side_pixels_y 个，和后 other_side_pixels_x 个 行
return U