221 lines
6.5 KiB
Python
221 lines
6.5 KiB
Python
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# -*- coding: UTF-8 -*-
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#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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'''
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@File : 多边形等距缩放.py
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@data ：2021/7/5 15:53
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@Desciption :
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@Version :
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@License :
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'''
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import cv2
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import numpy as np
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import time
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def contours_2_list(data):
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"""将opencv读取的边界numpy数组（3个维度）转化为list（2个维度）
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Args:
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data为数组
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Returns:
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list（2个维度）
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"""
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# yyy = data[0]
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yyy = data
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# print(yyy)
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list_contours = []
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for i in range(yyy.shape[0]):
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list_contours.append([yyy[i][0][0], yyy[i][0][1]])
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return list_contours
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def scale(data, sec_dis):
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"""多边形等距缩放
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Args:
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data: 多边形按照逆时针顺序排列的的点集
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sec_dis: 缩放距离
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Returns:
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缩放后的多边形点集
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"""
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num = len(data)
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scal_data = []
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for i in range(num):
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x1 = data[(i) % num][0] - data[(i - 1) % num][0]
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y1 = data[(i) % num][1] - data[(i - 1) % num][1]
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x2 = data[(i + 1) % num][0] - data[(i) % num][0]
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y2 = data[(i + 1) % num][1] - data[(i) % num][1]
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d_A = (x1 ** 2 + y1 ** 2) ** 0.5
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d_B = (x2 ** 2 + y2 ** 2) ** 0.5
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Vec_Cross = (x1 * y2) - (x2 * y1)
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if (d_A * d_B==0):
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continue
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sin_theta = Vec_Cross / (d_A * d_B)
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if (sin_theta==0):
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continue
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dv = sec_dis / sin_theta
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v1_x = (dv / d_A) * x1
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v1_y = (dv / d_A) * y1
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v2_x = (dv / d_B) * x2
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v2_y = (dv / d_B) * y2
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PQ_x = v1_x - v2_x
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PQ_y = v1_y - v2_y
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Q_x = data[(i) % num][0] + PQ_x
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Q_y = data[(i) % num][1] + PQ_y
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scal_data.append([Q_x, Q_y])
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return scal_data
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t1=time.time()
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# 1.读入图片
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# img = cv2.imread('demo/171.png')
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img = cv2.imread('demo1/10.png')
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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t2=time.time()
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contours, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# 2.寻找轮廓(多边界)
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contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, 2)
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t3=time.time()
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# 3.轮廓数组转为列表(多边界)
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list_contours=[]
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# xxx=contours[0]
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# yyy=contours[1]
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for i in range(len(contours)):
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list_contours.append(contours_2_list(contours[i]))
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# for i in range(yyy.shape[0]):
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# list_contours.append([yyy[i][0][0],yyy[i][0][1]])
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data=list_contours
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# 4.等距离扩大或缩小(多边界)
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data1=[]
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for i in range(len(data)):
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data1.append(scale(data[i],-15)) #减号是往外扩
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print(data1)
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t4=time.time()
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# 5.在原图尺寸上绘制出扩充后边界(多边界)
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# temp = np.ones((img_gray.shape[0],img_gray.shape[1],3), np.uint8) * 255 #白色底图
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# temp = np.zeros((img_gray.shape[0],img_gray.shape[1],3), np.uint8) * 255 #黑色底图
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temp = cv2.imread('demo1/10.jpg') #原图
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t5=time.time()
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# cv2.polylines(temp, data1, 1, 255)
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# cv2.polylines(temp,[np.array(data1, dtype=np.int32)],True, (255, 0, 0), 1,lineType=cv2.LINE_AA)
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for i in range(len(data1)):
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cv2.polylines(temp,[np.array(data1[i], dtype=np.int32)],True, (255, 0, 255), 5)
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# cv2.polylines(temp , [np.array(data1 , dtype=np.int32)], True, (0, 0, 255), 1)\
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t6=time.time()
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cv2.namedWindow("boundary",cv2.WINDOW_NORMAL);
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cv2.resizeWindow("boundary", 640, 480);
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cv2.imshow("boundary",temp);
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cv2.imwrite("demo1/24_detected.jpg",temp)
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t7=time.time()
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print('总耗时',t7-t1)
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print("读二值图像耗时:%s 形成轮廓耗时:%s 等距离缩放耗时:%s 读取原图：%s 绘制多段线: %s 保存图像耗时:%s" %(t2-t1,t3-t2,t4-t3,t5-t4,t6-t5,t7-t6))
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cv2.waitKey(0)
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# import os
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# from flask import Flask, request, redirect, url_for, jsonify
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# from werkzeug.utils import secure_filename
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#
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# import cv2
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# import numpy as np
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#
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# UPLOAD_FOLDER = './images'
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# CONVERTED_FOLDER = './static'
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# ALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])
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#
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# app = Flask(__name__, static_folder='static', static_url_path='/static')
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# app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
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# app.config['CONVERTED_FOLDER'] = CONVERTED_FOLDER
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#
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# def allowed_file(filename):
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# return '.' in filename and \
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# filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
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#
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# @app.route('/', methods=['POST'])
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# def upload_file():
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# if request.method == 'POST':
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# if 'file' not in request.files:
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# return jsonify(error = 'No file part')
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# file = request.files['file']
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#
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# if file.filename == '':
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# return jsonify(error = 'No selected file')
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# if file and allowed_file(file.filename):
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# filename = secure_filename(file.filename)
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# file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
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#
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# filename_no_ext = file.filename.split('.')[0]
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#
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# BLUR = 21
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# CANNY_THRESH_1 = 1
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# CANNY_THRESH_2 = 200
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# MASK_DILATE_ITER = 10
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# MASK_ERODE_ITER = 10
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# MASK_COLOR = (0.0,0.0,1.0)
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#
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# img = cv2.imread(os.path.join(app.config['UPLOAD_FOLDER'], filename))
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# gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
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#
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# edges = cv2.Canny(gray, CANNY_THRESH_1, CANNY_THRESH_2)
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# edges = cv2.dilate(edges, None)
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# edges = cv2.erode(edges, None)
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#
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# contour_info = []
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# img2, contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
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# for c in contours:
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# contour_info.append((
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# c,
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# cv2.isContourConvex(c),
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# cv2.contourArea(c),
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# ))
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# contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True)
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# max_contour = contour_info[0]
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#
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# mask = np.zeros(edges.shape)
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#
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# cv2.fillConvexPoly(mask, max_contour[0], (255))
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#
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# mask = cv2.dilate(mask, None, iterations=MASK_DILATE_ITER)
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# mask = cv2.erode(mask, None, iterations=MASK_ERODE_ITER)
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# mask = cv2.GaussianBlur(mask, (BLUR, BLUR), 0)
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#
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#
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# mask_stack = np.dstack([mask]*3)
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#
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# mask_stack = mask_stack.astype('float32') / 255.0
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# img = img.astype('float32') / 255.0
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#
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# masked = (mask_stack * img) + ((1-mask_stack) * MASK_COLOR)
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# masked = (masked * 255).astype('uint8')
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# c_red, c_green, c_blue = cv2.split(img)
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# img_a = cv2.merge((c_red, c_green, c_blue, mask.astype('float32') / 255.0))
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#
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# cv2.imwrite('./static/' + filename_no_ext + '.png', img_a*255)
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#
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# return jsonify(url = os.path.join(app.config['UPLOAD_FOLDER'],filename))
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#
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# app.run(host='0.0.0.0',threaded=True)
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