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python check_compress_psd.py zip
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pause
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# **ArtPuzzle 项目美术素材库**
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* ## **该项目作为美术存储PSD，打包前读取，并解析的源**<br><br>
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## **version: 0.0.1**
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- 用于存储美术相关的PSD源文件
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- 项目已开启LFS
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<br><br><br>
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# **目录结构**
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- ap_art_assets
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- art_assets
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- external
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- check_compress_psd.py
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- PSD检测-强制.bat
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- PSD检测.bat
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- draw_line.py
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- draw_outline.py
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- export_outline.py
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## 使用方法
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* Windows：双击PSD检测.bat
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* Mac命令行执行：
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```shell
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python3 checkout_compress_psd.py
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* 执行完成后，会将psd压缩成zip
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* 请手动上传zip至git源，请将version.json文件一起上传
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import cv2
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import numpy as np
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# 读取图像并将其转换为灰度图像
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img = cv2.imread('image.png')
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 查找轮廓
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contours, hierarchy = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# 绘制轮廓
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for contour in contours:
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# 计算轮廓长度
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contour_length = cv2.arcLength(contour, True)
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# 定义线条粗细的列表，使其从1到10逐渐增加
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thickness_list = np.linspace(1, 10, len(contour))
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# 遍历轮廓上的每个点，并在图像中绘制线条
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for i, point in enumerate(contour):
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# 计算当前点的线条粗细
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thickness = int(thickness_list[i])
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# 绘制线条
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if i == 0:
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# 如果是起点，线条粗细为1
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cv2.circle(img, tuple(point[0]), 1, (0, 0, 255), -1)
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else:
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# 如果不是起点，线条粗细为thickness
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prev_point = tuple(contour[i - 1][0])
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curr_point = tuple(point[0])
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cv2.line(img, prev_point, curr_point, (0, 0, 255), thickness)
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# 显示图像
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cv2.imshow('image', img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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#!/usr/bin/python
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#encoding:utf-8
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import math
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import cv2
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import numpy as np
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import os
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from PIL import Image
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import random
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def generate_picture(name, high, wide):
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"""生成图片"""
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# 白色透明背景
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img = np.ones((high, wide, 4)) * (255, 255, 255, 0)
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# print("name", name)
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# 保存为图片
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#cv2.imwrite(name, img)
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cv2.imencode('.png', img)[1].tofile(name)
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def gen_outline(path, dest, offset):
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img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
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img_contour = cv2.imread(dest, cv2.IMREAD_UNCHANGED)
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kernel_size = 1
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sigma = 0.5
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kernel = cv2.getGaussianKernel(kernel_size, sigma)
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kernel = np.dot(kernel, kernel.T)
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smoothed = cv2.filter2D(img, -1, kernel)
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_, thresh = cv2.threshold(smoothed, 1, 255, cv2.THRESH_BINARY)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 计算轮廓的总面积
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total_area = 0
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for contour in contours:
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area = cv2.contourArea(contour)
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total_area = total_area + area
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# 选择周长和面积满足条件的轮廓，过滤太小的轮廓
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valid_contours = []
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threshold = 0.5
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idx = 0
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while not (len(valid_contours) > 0 and idx == 0):
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contour = contours[idx]
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perimeter = cv2.arcLength(contour, True)
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area = cv2.contourArea(contour)
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# print(path, "周长 =", perimeter, "面积占比 =", area/total_area, threshold)
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if perimeter > 0 and area/total_area > threshold:
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# print("add =====> ")
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valid_contours.append(contour)
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idx = idx + 1
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if idx >= len(contours):
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threshold = threshold - 0.1
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idx = 0
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total = len(valid_contours[0])
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start = random.randint(0, int(total/3*2))
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# start = 0
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end = start + int(total/3)
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# end = total
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contour_points = valid_contours[0][start:(end)]
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pixle_offset = (int(offset / 2), int(offset / 2))
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offset_contour_points = contour_points + pixle_offset
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cv2.polylines(img_contour, [offset_contour_points], False, (255, 255, 255, 255), int(offset - 1), lineType=cv2.LINE_AA)
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cv2.polylines(img_contour, [offset_contour_points], False, (0, 0, 0, 255), int(offset / 2 - 1), lineType=cv2.LINE_AA)
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cv2.imwrite(dest, img_contour)
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def draw_outline(path, offset, order):
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img = cv2.imread(path)
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h, w, c = img.shape
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width = w + offset
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height = h + offset
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img_outline_path = path.replace(".png", "_outline.png")
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generate_picture(img_outline_path, height, width)
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gen_outline(path, img_outline_path, offset)
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return img_outline_path
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def paste(src, dest, x, y):
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img2 = Image.open(dest)
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img2 = img2.convert("RGBA")
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img1 = Image.open(src)#小图路径
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img1 = img1.convert("RGBA")
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img2.paste(img1, (x, y), mask=img1)
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img2.save(dest)
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if __name__ == "__main__":
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path = "/Users/mac/Documents/guru/unity_ap_bundle/ArtPuzzleBundle/Assets/Bundles/Levels/28/Atlas"
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generate_picture("line_1.png", 1280, 1040)
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os.system("cd {} && rm *_outline.png".format(path))
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for root , dirs, files in os.walk(path):
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for name in files:
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if "patch_" in name and name.endswith(".png") and "outline" not in name:
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# print("name = {}".format(name))
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draw_outline(os.path.join(root, name), 3)
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#!/usr/bin/python
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#encoding:utf-8
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import os, sys
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import time
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import cv2
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from psd_tools import PSDImage
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from PIL import Image, ImageFilter
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import numpy as np
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import shutil
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curr_dir = os.path.split(os.path.abspath(__file__))[0]
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sys.path.append(os.path.join(curr_dir,'../../'))
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import utils
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import json
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import draw_outline
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levels_root_path = os.path.join(curr_dir, "temp/Levels/")
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external_root_path = os.path.join(curr_dir, "external")
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version_path = os.path.join(curr_dir, "psd_outline_version.json")
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PSD_DESIGN_WIDTH = 2080
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PSD_DESIGN_HEIGTH = 2560
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TOTAL_OFFSET_ORIGIN = 16
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TOTAL_OFFSET = TOTAL_OFFSET_ORIGIN
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#缩略图尺寸
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thumbnail_width = 480
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scale = 0
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versions = {}
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def del_lines(psd_name):
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dir_name = os.path.join(external_root_path, psd_name, "line")
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if not os.path.exists(dir_name):
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os.makedirs(dir_name)
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# .system("rm -rf {}".format(dir_name))
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shutil.rmtree(dir_name)
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# def get_or_create_line_png(psd_name, num, sub, width, height):
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# dir_name = os.path.join(external_root_path, psd_name, "line")
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# line_path = os.path.join(dir_name, f"line_{num}_{sub}.png")
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# if not os.path.exists(dir_name):
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# os.makedirs(dir_name)
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# if not os.path.exists(line_path):
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# draw_outline.generate_picture(line_path, height + TOTAL_OFFSET, width + TOTAL_OFFSET)
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# return line_path
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def crop_image(path, dest_path, new_height, new_width):
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re_img = Image.open(path)
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re_img = Image.fromarray(np.uint8(re_img))
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scalex = thumbnail_width / re_img.width
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scaley = thumbnail_width / re_img.height
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scale = max(scalex, scaley)
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re_img = re_img.resize((int(re_img.width * scale), int(re_img.height * scale)), Image.ANTIALIAS)
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width, height = re_img.size
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left = (width - new_width)/2
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top = (height - new_height)/2
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right = (width + new_width)/2
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bottom = (height + new_height)/2
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crop_im = re_img.crop((left, top, right, bottom))
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crop_im.save(dest_path, "png")
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def export_outline(path):
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global scale
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global versions
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global TOTAL_OFFSET
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file_name = os.path.split(path)[1]
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psd = PSDImage.open(path)
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max_width = 0
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max_height = 0
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(filepath, filename) = os.path.split(path)
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(psd_filename, filename_ext) = os.path.splitext(filename)
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#只取前缀
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filename_only = psd_filename.split("_")[0]
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#删除所有线条文件，重新生成
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del_lines(psd_filename)
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for item in psd.descendants():
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if not item.is_group():
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nArr = item.name.split('_')
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if item.name == "base":
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max_width = item.width
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max_height = item.height
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break
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TOTAL_OFFSET = int(TOTAL_OFFSET_ORIGIN * (max_width / PSD_DESIGN_WIDTH))
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for item in psd.descendants():
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if not item.is_group():
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item.visible = True
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#按照原始比例导出
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scale = 1
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nArr = item.name.split("_")
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folder_path = ""
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base_folder_name = ""
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file_path = ""
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save_path_pre = ""
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item_name = item.name
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mid_path = filename_only
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if nArr[0] == "base" or nArr[0] == "line" or nArr[0] == "finish" or nArr[0] == "auto" or nArr[0] == "role":
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continue
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elif nArr[0] == "patch":
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base_folder_name = "Atlas"
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save_path_pre = levels_root_path
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elif nArr[0] == "thumbnail":
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continue
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folder_path = os.path.join(save_path_pre, mid_path, base_folder_name)
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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file_path = os.path.join(folder_path, "{}.png".format(item_name))
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item.composite().save(file_path, compress_level=1, format='PNG', use_channel="RGBA", quality=95)
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# #缩略图不需要压缩
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if nArr[0] != "thumbnail":
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#根据设计分辨率重置尺寸
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img = Image.open(file_path).convert('RGBA')
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img = img.filter(ImageFilter.GaussianBlur(radius=1))
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#防止无改变
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img = img.resize((int(item.width * scale) + 1, int(item.height * scale) + 1), Image.ANTIALIAS)
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img.save(file_path, "png")
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#防止无改变
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img = img.resize((int(item.width * scale), int(item.height * scale)), Image.ANTIALIAS)
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# img.save(file_path, "png")
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# 打开图像文件并转换为 NumPy 数组
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image = np.array(img.convert("RGBA"))
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# 获取 alpha 通道数据
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alpha = image[:, :, 3]
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# 将 alpha 值小于等于 10 的像素的 alpha 值设为 0
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alpha[alpha <= 20] = 0
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# 将修改后的 alpha 通道数据保存回原始数组
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image[:, :, 3] = alpha
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# 将修改后的 NumPy 数组转换回图像并保存
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Image.fromarray(image).save(file_path)
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if nArr[0] == "patch":
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outline_file_path = draw_outline.draw_outline(file_path, TOTAL_OFFSET, int(nArr[2]))
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dir_name = os.path.join(external_root_path, psd_filename, "line")
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if not os.path.exists(dir_name):
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os.makedirs(dir_name)
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line_path = os.path.join(dir_name, f"line_{nArr[1]}_{nArr[2]}.png")
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shutil.copy(outline_file_path, line_path)
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# draw_outline.paste(outline_file_path, line_path, 0, 0)
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shutil.rmtree(levels_root_path)
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###生成version文件
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def make_version_file(remake):
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version_file = os.path.join(curr_dir, version_path);
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if remake:
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if os.path.exists(version_file):
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os.remove(version_file)
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if not os.path.exists(version_file):
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with open(version_file, "w") as f:
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json.dump({}, f)
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###加载版本文件
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def load_version():
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global versions
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make_version_file(False);
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version_file = os.path.join(curr_dir, version_path);
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with open(version_file, "r") as f:
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try:
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versions = json.load(f)
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except:
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#删除版本文件并重新生成
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make_version_file(True);
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def write_version():
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version_file = os.path.join(curr_dir, version_path);
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with open(version_file, "w") as f:
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json.dump(versions, f)
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def parse_psd(path):
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load_version()
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for root , dirs, files in os.walk(path):
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for name in files:
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if name.endswith(".psd") or name.endswith(".zip"):
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src_filename = os.path.join(root, name)
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file_name = os.path.split(src_filename)[1]
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key = file_name.split("_")[0]
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hash_old = ""
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if key in versions:
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hash_old = versions[key]
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hash_now = utils.calc_hash(src_filename)
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if hash_old != hash_now:
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#如果是压缩文件，则先解压
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if name.endswith(".zip"):
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psd_zip_filename = os.path.join(root, name)
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utils.unzip_file(psd_zip_filename)
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psd_filename = "{}.psd".format(os.path.splitext(os.path.join(root, name))[0])
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else:
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psd_filename = src_filename
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try:
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export_outline(psd_filename)
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versions[key] = hash_now
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write_version();
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except Exception as e:
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print(name + " 解析失败：" + repr(e))
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finally:
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#最后删除psd文件
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if psd_filename != "":
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os.remove(psd_filename)
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if __name__ == "__main__":
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parse_psd("./")
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Binary file not shown.
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After Width: | Height: | Size: 8.8 KiB |
Binary file not shown.
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After Width: | Height: | Size: 245 KiB |
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|
|||
{"10": "4bb04d8d4c018f1df36c1b91d1310ddc4f86f21a", "11": "a1fe8ee77712a0a6f1c465d9bc6cacf33acf8e3d", "13": "9ddde32e5a8f88559aba8e003225a6bbf3046569", "14": "ad09d851809ab59342639536d1f7f09ff13acdd2", "15": "cb06015a06f0e33c71b318a7fee78ef90432df61", "12": "0347d9a847c5257149c38f2fadceb968279e4b57", "16": "816522fa8306175b1bf3dbf8d0a32cca934decec", "17": "84a41dbe457701d61a94f8ceaf65ac7968ea63e8", "18": "a79f514ec31a8fce7bbb8498742db4af144c5448", "19": "9445da20ea1f93ef5f6879e4c5c4a1be9b9a741c", "1": "2d190da2f8153755d4c99837380f4b3d523c7377", "20": "61b520498f18e4402db58efee9f43d3d70028234", "21": "96ce013f02076a8976119fde88b0b27683dc71c6", "22": "a38de779e8085654bb334626b5f88926e8a8c535", "23": "03a05c6545b3f5434f5bc2d69e4e1cbb0da392d2", "24": "5647e7eb865dafc7787986e4658d7805f7b212c2", "25": "3032f1f1f3a54847693d4e481211eb3665850a08", "26": "c4832180e57c6e9744c6ca754539eab30954aa2d", "27": "5956a7953cec77cf8ed2541386f3a3c84a6d109f", "28": "e39256ac7248496ea638bfaeea81897cdcf24784", "29": "dcc1c1094d320c82104d01b96e440d55f5db523b", "2": "75ed939866284800222292bc75f59c33afb1eb30", "30": "5ca514eb377ac15f060b62129f5cf7b56ce736ad", "31": "f9c80a1d9431baff3763fa43381e71777e49ea63", "32": "f0048d11636646afbf240e460bc4506f23c6c6e7", "33": "06a29d90d0903ea83166386ac4cb9f12261164f2", "34": "a7e6f322235e2832e55d52c8096473e2aa81be69", "35": "fbe4a5d8532acc124489b15940b0c5103fa2aba5", "36": "069873dea12ef540a6b96341aa2281dbb14a2d9b", "37": "eb7d90e157194e5ae1a779fe081b6868aa952e6d", "38": "a53853704c75cf1977f1cd57d67a76b1d2f52f15", "39": "a6568998b8093949a85315013843f97b8f4d7f95", "3": "bb4409b1d925a9e3d35213dfe201e7593ec1eb84", "40": "567d48db99de821bb6ffbe7975752bf9e8b0f0cc", "41": "f07e7306d06ce47e259cae693c39f1e8a5ff0ffc", "42": "2bf2250859e6948c2a090f1d9a6fc7b21771173b", "43": "5a4b4e8e55b730b310764b8b2e18379b6868b5a7", "44": "72163df8c065baa02f589c88260892f549e8cf59", "45": "b1e3150ef7ea0258e27007b510ac0ee0fc46f319", "46": "a742c394419fe628b2fa38c689e57c1b0f678ac1", "47": "60197b4a939ef4984fef94b0d30f1a2321fe9fb3", "48": "6b8e5b9966fea194ff01e443e030282b091331d4", "49": "66953b7e09879ed4283bad3a0cdd79dfb8c78c3f", "4": "3fa653912d2ea2269bbd1b0dfb065366af466476", "50": "db96e7a7d90a46ccc3bf7deda473358a500e380e", "51": "3f0740cb81423c718690868576631272a425c7f4", "52": "1a7626dcab0860298f346126cffa8e0c852e5cdf", "53": "c6192c45d75674631e5323e6983e66d5887fe38a", "54": "506f5a1b7b738f698d6d63b5c30ba3b21af31dab", "55": "e19b93a609d93e6e9c299c8e474bb3ddf0c707f8", "56": "1b2db8271922882dabea599352e4ae60dd7aaed3", "57": "fc43a804c1f4f142be064f0542b652deefe3a465", "58": "d81bdf622f10835b09fb5a9b7ede5f84d3253020", "59": "5e240e26b788e61162a117013716402bb15a5f81", "5": "518cedc79bf790c824a78629c92bc7a0823b4f47", "60": "ee741b0e14b874318723b520750540e6b984b10a", "61": "a6366845359f345cbff1b0b9eee91cc26294bcca", "62": "c2c33299c4f891a244a76dd98d95facbca2d7fbb", "63": "11f55de674ff77afdc1c6d3013bf2ba70df58a69", "64": "f83f6beca7c6471d1dc274e396bf5b816ed784be", "65": "a9768d468076ea5cf41bbbac2bd83fca5519fc2c", "66": "8d3e02c3ea2e4eb5004f05fdcf11fa09a72bf579", "67": "cdc652281ee5260017e759974fd50834f9e8afea", "68": "cb9599aac4098fa2d6b36751b74be7e08acd1488", "69": "29c3b755600564108b8c3bcbfe75abc95b502c5c", "6": "5430c8aab736fe156e267734cbc2b85a4c2ef462", "70": "b64b7ffa9349cf6dfbde88705c2772750c17b2df", "71": "2795004b6ec360dae7aec1fac200f7c65b109055", "72": "b915932ab39a6395c0e4db79306171e36259e7a0", "73": "702071364d41da16ee54887c19d14601dcd2ce66", "7": "548556e14bbe49454d9bac7dea4bfccf8da022f0", "8": "4664c0a2a4152bfa9ea17ee2dc98d976488664be", "9": "bcc0acd0c8e3ff834f6c9fa68e3fb2eea806fe8b"}
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
import math
|
||||
import random
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
TOTAL_OFFSET = 8
|
||||
|
||||
def gen_outline(path, dest):
|
||||
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
|
||||
img_contour = cv2.imread(dest, cv2.IMREAD_UNCHANGED)
|
||||
|
||||
kernel_size = 1
|
||||
sigma = 0.5
|
||||
kernel = cv2.getGaussianKernel(kernel_size, sigma)
|
||||
kernel = np.dot(kernel, kernel.T)
|
||||
smoothed = cv2.filter2D(img, -1, kernel)
|
||||
|
||||
_, thresh = cv2.threshold(smoothed, 1, 255, cv2.THRESH_BINARY)
|
||||
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
||||
|
||||
# 计算轮廓的总面积
|
||||
total_area = 0
|
||||
for contour in contours:
|
||||
area = cv2.contourArea(contour)
|
||||
total_area = total_area + area
|
||||
|
||||
# 选择周长和面积满足条件的轮廓，过滤太小的轮廓
|
||||
valid_contours = []
|
||||
threshold = 0.5
|
||||
idx = 0
|
||||
while not (len(valid_contours) > 0 and idx == 0):
|
||||
contour = contours[idx]
|
||||
perimeter = cv2.arcLength(contour, True)
|
||||
area = cv2.contourArea(contour)
|
||||
# print(path, "周长 =", perimeter, "面积占比 =", area/total_area, threshold)
|
||||
if perimeter > 0 and area/total_area > threshold:
|
||||
# print("add =====> ")
|
||||
valid_contours.append(contour)
|
||||
idx = idx + 1
|
||||
if idx >= len(contours):
|
||||
threshold = threshold - 0.1
|
||||
idx = 0
|
||||
|
||||
start = random.randint(0, int(len(valid_contours[0])/3*2)-1)
|
||||
start = 0
|
||||
contour_points = valid_contours[0][start:(start + int(len(valid_contours[0])/3))]
|
||||
contour_points = valid_contours[0][start:(start + len(valid_contours[0]))]
|
||||
offset = (4, 4)
|
||||
offset_contour_points = contour_points + offset
|
||||
print("contour_points start = ", start, len(valid_contours[0]))
|
||||
cv2.polylines(img_contour, [offset_contour_points], False, (255, 255, 255, 255), int(TOTAL_OFFSET - 1), lineType=cv2.LINE_AA)
|
||||
cv2.polylines(img_contour, [offset_contour_points], False, (0, 0, 0, 255), int(TOTAL_OFFSET / 2 - 1), lineType=cv2.LINE_AA)
|
||||
|
||||
# cv2.drawContours(img_contour, valid_contours, -1, (255, 255, 255, 255), 9, None, None, None, (5, 5))
|
||||
# cv2.drawContours(img_contour, valid_contours, -1, (0, 0, 0, 255), 5, None, None, None, (5, 5))
|
||||
# cv2.imwrite(dest, img_contour)
|
||||
|
||||
cv2.imshow('Original Image', img)
|
||||
cv2.imshow('Contour Image', img_contour)
|
||||
cv2.waitKey(0)
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
if __name__ == "__main__":
|
||||
gen_outline("./image.png", "./image-emt.png")
|
||||
|
|
@ -0,0 +1,154 @@
|
|||
#!/usr/bin/env python
|
||||
#coding:utf-8
|
||||
|
||||
import datetime
|
||||
import os
|
||||
import zipfile
|
||||
import subprocess
|
||||
import json
|
||||
import hashlib
|
||||
|
||||
def zip_dir(dirpath, out_fullname):
|
||||
"""
|
||||
压缩指定文件夹
|
||||
:param dirpath: 目标文件夹路径
|
||||
:param outFullName: 压缩文件保存路径+xxxx.zip
|
||||
:return: 无
|
||||
"""
|
||||
zip = zipfile.ZipFile(out_fullname,"w",zipfile.ZIP_DEFLATED)
|
||||
for path,dirnames,filenames in os.walk(dirpath):
|
||||
# 去掉目标跟路径，只对目标文件夹下边的文件及文件夹进行压缩
|
||||
fpath = path.replace(dirpath,'')
|
||||
|
||||
for filename in filenames:
|
||||
zip.write(os.path.join(path,filename),os.path.join(fpath,filename))
|
||||
zip.close()
|
||||
|
||||
def unzip_dir(zip_src, dst_dir):
|
||||
"""
|
||||
解压文件到指定文件夹
|
||||
:param zip_src: zip文件
|
||||
:param dst_dir: 解压目录
|
||||
:return: 无
|
||||
"""
|
||||
r = zipfile.is_zipfile(zip_src)
|
||||
if r:
|
||||
fz = zipfile.ZipFile(zip_src, 'r')
|
||||
for file in fz.namelist():
|
||||
fz.extract(file, dst_dir)
|
||||
else:
|
||||
print("{}不是zip文件".format(zip_src))
|
||||
|
||||
def zip_file(file_path, out_fullname, is_fixedtime = True):
|
||||
"""
|
||||
压缩指定文件
|
||||
:param dirpath: 目标文件路径
|
||||
:param outFullName: 压缩文件保存路径+xxxx.zip
|
||||
:return: 无
|
||||
"""
|
||||
zip_file = zipfile.ZipFile(out_fullname,'w')
|
||||
zip_file.write(file_path, compress_type=zipfile.ZIP_DEFLATED)
|
||||
if is_fixedtime:
|
||||
# 分离文件名称
|
||||
names = zip_file.namelist()
|
||||
for a_name in names:
|
||||
zipinfo_obj = zip_file.getinfo(a_name)
|
||||
file_stat = os.stat(file_path)
|
||||
zipinfo_obj.date_time = datetime.datetime.fromtimestamp(file_stat.st_mtime).timetuple()[:6]
|
||||
|
||||
zip_file.close()
|
||||
def unzip_file(zip_file_path):
|
||||
"""
|
||||
解压文件
|
||||
:param zip_src: zip文件
|
||||
:return: 无
|
||||
"""
|
||||
zip_file = zipfile.ZipFile(zip_file_path)
|
||||
|
||||
# 解压
|
||||
# zip_file.extractall(path="{}/../".format(os.path.splitext(zip_file_path)[0]))
|
||||
|
||||
# 设置要解压缩的文件和目录
|
||||
extract_dir = "{}".format(os.path.split(zip_file_path)[0])
|
||||
|
||||
# 解压缩文件并将修改时间设置为原有时间
|
||||
with zipfile.ZipFile(zip_file_path, 'r') as zip_file:
|
||||
for zip_info in zip_file.infolist():
|
||||
zip_file.extract(zip_info.filename, path=extract_dir)
|
||||
file_path = os.path.join(extract_dir, zip_info.filename)
|
||||
mtime = int(datetime.datetime(*zip_info.date_time).timestamp())
|
||||
os.utime(file_path, (mtime, mtime))
|
||||
|
||||
|
||||
def DoCmd(strcmd, logPath=""):
|
||||
if len(logPath) > 1:
|
||||
logfile = file(logPath, "a")
|
||||
logfile.writelines("-----------------------------")
|
||||
logfile.writelines(strcmd)
|
||||
|
||||
process = subprocess.Popen(
|
||||
strcmd, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
||||
print(strcmd)
|
||||
lines_out = process.stdout.readlines()
|
||||
for l in lines_out:
|
||||
print(l)
|
||||
if len(logPath) > 1:
|
||||
logfile.writelines(l)
|
||||
|
||||
|
||||
lines_error = process.stderr.readlines()
|
||||
if len(logPath) > 1 and len(lines_error)>0:
|
||||
logfile.writelines("has error:\n\n")
|
||||
for l in lines_error:
|
||||
print(l)
|
||||
if len(logPath) > 1:
|
||||
logfile.writelines(l)
|
||||
|
||||
print("end: "+strcmd)
|
||||
if len(logPath) > 0:
|
||||
logfile.writelines("end: "+strcmd)
|
||||
logfile.close()
|
||||
#
|
||||
return lines_out, lines_error
|
||||
|
||||
def get_file_last_line(fname):
|
||||
"""
|
||||
f_name为所读xx.txt文件
|
||||
输出为：文件最后一行
|
||||
"""
|
||||
print(fname)
|
||||
with open(fname, 'r') as f: #打开文件
|
||||
first_line = f.readline() #读第一行
|
||||
off = -50 #设置偏移量
|
||||
while True:
|
||||
f.seek(off, 2) #seek(off, 2)表示文件指针：从文件末尾(2)开始向前50个字符(-50)
|
||||
lines = f.readlines() #读取文件指针范围内所有行
|
||||
if len(lines)>=2: #判断是否最后至少有两行，这样保证了最后一行是完整的
|
||||
last_line = lines[-1] #取最后一行
|
||||
break
|
||||
#如果off为50时得到的readlines只有一行内容，那么不能保证最后一行是完整的
|
||||
#所以off翻倍重新运行，直到readlines不止一行
|
||||
off *= 2
|
||||
|
||||
print('文件' + fname + '第一行为：' + first_line)
|
||||
print('文件' + fname + '最后一行为：'+ last_line)
|
||||
return last_line
|
||||
|
||||
def open_json(path):
|
||||
dic = {}
|
||||
with open(path, 'r') as f:
|
||||
dic = json.load(f)
|
||||
|
||||
return dic
|
||||
|
||||
def write_json(path, content):
|
||||
with open(path, 'w') as f:
|
||||
json.dump(content, f)
|
||||
|
||||
#根据文件内容，生成hash值，filepath文件路径
|
||||
def calc_hash(filepath):
|
||||
with open(filepath,'rb') as f:
|
||||
sha1obj = hashlib.sha1()
|
||||
sha1obj.update(f.read())
|
||||
hash = sha1obj.hexdigest()
|
||||
return hash
|
||||
|
|
@ -0,0 +1,934 @@
|
|||
{
|
||||
"1": "85d374f8b8cf9be98fcc73aca358bc7a46b76eb9",
|
||||
"10": "bff3c44bdb4d9fbd400c03caa5542c7e96dd20fc",
|
||||
"100": "4f2e650222b8a35caabcb3a40ae70a06be569ca5",
|
||||
"101": "654f3feaa984024f8ac981b615391ad0c9c0f0c1",
|
||||
"102": "6dfce2d243a95239332df3169f099b9dbf94deb2",
|
||||
"103": "6b007b99fae0e2c4d7ff00407e445b0680a5f365",
|
||||
"104": "207134aac4c3f227ed256e498f12e401d6cfc222",
|
||||
"105": "447304251d5e5be46468cb81c511ed70a62a2b57",
|
||||
"106": "f275a936af0a5235da2026eebd69bbd757290f63",
|
||||
"107": "8853c3868c1b0b66b8f176641aaba1d4ade4e3c2",
|
||||
"108": "95ccb7718587315765ea83b2e1c8d54910ba5d00",
|
||||
"109": "3687f635fa57260a766c35da73abfedb510526d5",
|
||||
"11": "0338fda17a17f605fc30d3957e126133a603d98f",
|
||||
"110": "497a982973130109d518c14ac1c8595719a479d8",
|
||||
"111": "192988725767a710427300e91c797cd3aee08a3b",
|
||||
"112": "c82e852a80fb7966654384357ba12587315428ff",
|
||||
"113": "2f2cee67b6c6311704db44f666be11b4c4fa43d2",
|
||||
"114": "bc1c701ec0cfcb969f59183d07a3c602bc1d1ea5",
|
||||
"115": "78d3b3cb2e9b96af1220bafa275df7ba64e6725d",
|
||||
"116": "8d9252a2ab238ef88b127afab4580a2c96df8201",
|
||||
"117": "e558e33e591b5a0940291fcddf9deae229c3cf83",
|
||||
"118": "d074866f55148a903007e321da1c28966c6e4777",
|
||||
"119": "ae6f3417a78d5cfcae24300d6fb58957b70c55a0",
|
||||
"12": "59f56158ec32d2165c364c3741f5ffea9417af57",
|
||||
"120": "f329397add79d3ba502146bf7c58f4c277b058e4",
|
||||
"121": "018fb17d5fcb0f98ffaa93cf0392925851495d0d",
|
||||
"122": "a0513c6acb62ac73a6ec04b2ef89414e99405dd1",
|
||||
"123": "34b012b4b1471d4aefe633c5f844a5eeea0e095c",
|
||||
"124": "047b1bb3f96a1f9d759a00c5f009708f80d03b68",
|
||||
"125": "2f82211de945775732af77cff6f2b643b6c980b8",
|
||||
"126": "17832186130d89b6a10de112418d724780a9c3e9",
|
||||
"127": "822af7c7ed66ef341f82116992f9f2844cb23355",
|
||||
"128": "d614249735920adbced679e54e4288e767aa9227",
|
||||
"129": "d0f5af7b76a8b02cbe36788b49c0ce7c71aaa0ca",
|
||||
"13": "499aac170d6a52b55c29ae4463e71ae9f702f8fb",
|
||||
"130": "82505fcc44c8a63739db080b4e7a1d698928b0e9",
|
||||
"131": "1546a8037ac030dfc3eee8246d6dfb1d0e6046f5",
|
||||
"132": "a43f9ec419efad47332676b5e88e754144797f0c",
|
||||
"133": "ea833a35d38419fcc99a14c005abe5d4f9281ebd",
|
||||
"134": "d8e07f03ffb03682ddff60e860ee1e8490b1d6f5",
|
||||
"135": "b1af0c0c10a890b5d67f37cf43a673c5caabb396",
|
||||
"136": "6d15f218ff643a5c278973108b5f394374fdaaef",
|
||||
"137": "1cb6d1fbcd5a60cec68b0bcead1b3bf73abe9e28",
|
||||
"138": "633c132789de2f3c2f39d6fe38cfe5ca6ed8324f",
|
||||
"139": "52377d22235fa80679b8d2c4e69e744982c3f6dd",
|
||||
"14": "f41eec957fbd8f7cdf97e043f8d454edd1352fb0",
|
||||
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||||
"913": "c24d7800cd856e4d597d0183d896fb05760d1f3e",
|
||||
"914": "33abd945424f901f8863595220cca972354e3622",
|
||||
"915": "137681c66c4a7ce01c34a32e75d87e072acd88ea",
|
||||
"916": "385d0029158eb2ff90b660ccf96431d3a9eecaec",
|
||||
"917": "4c1b31dc937128fd40b3ac29af859a04298cbd08",
|
||||
"918": "344db117ad89fb5b6f50664bc02b4cb6584045ea",
|
||||
"919": "92a8954327978306804e8e0df94f908950f32950",
|
||||
"92": "31f8b5b47827a632a98997f6de97fa34b03abd15",
|
||||
"920": "f8725ab46b0d9b456532f55e5036892fb4a16451",
|
||||
"921": "a4a6d21e5c70bebe6c0c0ec64170cbf99d9bb8e5",
|
||||
"922": "9cd7f43a893c8ab97e3251a9813558a07eb7f895",
|
||||
"923": "d9ebd09a10cf3cd32f051380bf8ec65a30fd282e",
|
||||
"924": "99c5361da6e6e3492270a42d924787702303e7c6",
|
||||
"925": "b7a177ee5e85401bfbb656c4824d3170f7a2e8b6",
|
||||
"928": "03e5449f0febb316c16d8516952fa9c194811378",
|
||||
"929": "bbfc4dd1f9811b3b947c85322a59d0e6bebe4968",
|
||||
"93": "28f4655991d56818257cb62850e8c28974e1e923",
|
||||
"930": "bfbab5489c9a2b540f7b10843e7db0e23bc486fa",
|
||||
"931": "8603e36f94f4bc854478f7a5014fa5a1ce24db33",
|
||||
"932": "d27a0c01e80cc4267bd4a34aa058d96d950c9ca9",
|
||||
"934": "fc7a981dacb34827a826400e2168986ba8bc2350",
|
||||
"94": "9dc16089869e4b01e381c6dafef33922d77f047b",
|
||||
"940110": "005a8d664e58ee2096c96830ac7c8868136123db",
|
||||
"95": "7ce62babb637b1a458082f15a9bccd26841683a2",
|
||||
"96": "e566d525d865ac9b06932f1af724dba21133a415",
|
||||
"97": "47e26056e431634e974ede5201131f1824074b40",
|
||||
"98": "fa4a9387170263ccfd549e2e1322dba4088e00eb",
|
||||
"99": "a1a72970d0dec0e3466ff91b37b82693ca362714"
|
||||
}
|
||||
Loading…
Reference in New Issue