基于机器视觉实现昆虫识别计数系统python源码+数据集+模型+详细项目说明.zip

# coding=utf-8 # 先读图，然后二值化, # 矩形度, import cv2 import math import numpy as np # 此处读入图片，作为接口 pic_name = 'picture/butterfly0.jpg' origin = cv2.imread(pic_name) grayimage = cv2.imread(pic_name, 0) # species = 'wo' # 　高斯滤波 blur = cv2.GaussianBlur(grayimage, (5, 5), 0) # 　二值化：用大津法，此处选项若是THRESH_BINARY_INV，则同意选用白色背景的图片样本 ret, otsu = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) # 找轮廓 contours = cv2.findContours(otsu, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 轮廓集数目 largest_area = 0 largest_contour_index = 0 num = len(contours[1]) for i in range(num): area = cv2.contourArea(contours[1][i], False) if area > largest_area: largest_area = area largest_contour_index = i maxContour = contours[1][largest_contour_index] # 画轮廓 cv2.drawContours(origin, maxContour, -1, (0, 0, 255), 2) print "最大面积" + str(largest_area) # 矩形度***************************************************** # 矩形度***************************************************** # 矩形度***************************************************** # 矩形度***************************************************** # 查找最小外接矩形 minAreaRect = cv2.minAreaRect(maxContour) box = cv2.boxPoints(minAreaRect) box = np.int0(box) # 画轮廓 cv2.drawContours(origin, [box], 0, (0, 255, 0), 2) # 计算最小外接矩形面积 minAreaRect_Area = int(cv2.contourArea(box, False)) print "最小外接矩形面积" + str(minAreaRect_Area) # 特征一：矩形度的计算 P_Rect = largest_area * 1.0 / minAreaRect_Area # 统一结果为3位小数 P_Rect = round(P_Rect, 3) print "矩形度" + str(P_Rect) # 延长度*************************************************************************************************** # 延长度*************************************************************************************************** # 延长度*************************************************************************************************** # 延长度*************************************************************************************************** # 质心计算 M = cv2.moments(maxContour) Centroid_x = int(M['m10'] / M['m00']) Centroid_y = int(M['m01'] / M['m00']) print "质心" + str(Centroid_x) + " " + str(Centroid_y) cv2.circle(origin, (Centroid_x, Centroid_y), 3, (255, 255, 255), -1) # 获取长轴 Major_Axis_Length = 0 Major_Axis_Angle = 0 Major_Axis_End_x = 0 Major_Axis_End_y = 0 Major_Axis_Begin_x = 0 Major_Axis_Begin_y = 0 # 此处需要注意质心是否在轮廓内 # 找长轴 for angle in range(180): theta = angle * 3.14 / 180.0 lengthBackward = 0 lengthForward = 0 point_End_x = Centroid_x point_End_y = Centroid_y point_Begin_x = Centroid_x point_Begin_y = Centroid_y # 步进越小准确率越高，设置成1可以先找到准确的直线角度，再根据角度和质心得到的直线计算出正确的长轴和轮廓交点 while cv2.pointPolygonTest(maxContour, (point_End_x, point_End_y), False) > 0: lengthForward = lengthForward + 0.1 point_End_x = int(round(point_End_x + lengthForward * math.cos(theta))) point_End_y = int(round(point_End_y + lengthForward * math.sin(theta))) while cv2.pointPolygonTest(maxContour, (point_Begin_x, point_Begin_y), False) > 0: lengthBackward = lengthBackward + 0.1 point_Begin_x = int(round(point_Begin_x - lengthBackward * math.cos(theta))) point_Begin_y = int(round(point_Begin_y - lengthBackward * math.sin(theta))) if lengthForward + lengthBackward >= Major_Axis_Length: Major_Axis_Length = lengthForward + lengthBackward Major_Axis_Angle = angle Major_Axis_End_x = point_End_x Major_Axis_End_y = point_End_y Major_Axis_Begin_x = point_Begin_x Major_Axis_Begin_y = point_Begin_y # 计算实际长轴长度 Real_Major_Axis_Length = math.sqrt( math.pow((Major_Axis_End_x - Major_Axis_Begin_x), 2) + math.pow((Major_Axis_End_y - Major_Axis_Begin_y), 2)) Real_Major_Axis_Length = round(Real_Major_Axis_Length, 1) print "长轴长度 = " + str(Real_Major_Axis_Length) print "长轴角度 = " + str(Major_Axis_Angle) # print "起点 = " + "x: " + str(Major_Axis_Begin_x) + " y: " + str(Major_Axis_Begin_y) # print "起点 = " + "x: " + str(Major_Axis_End_x) + " y: " + str(Major_Axis_End_y) # 画长轴 cv2.line(origin, (Major_Axis_Begin_x, Major_Axis_Begin_y), (Major_Axis_End_x, Major_Axis_End_y), (255, 0, 0), 2) # 找短轴 # 1. 先得到长轴直线的表达式y=k*x+b，用来计算点到直线距离和判断点在直线上方还是下方 Major_Axis_k = math.tan(Major_Axis_Angle * 3.14 / 180.0) Major_Axis_b = Centroid_y - Major_Axis_k * Centroid_x # 2. 点(x0,y0)到直线(Ax+By+C=0)的距离为d =abs (A*x0+B*y0+C)/sqrt(A^2+B^2) Minor_Axis_A = Major_Axis_k Minor_Axis_B = -1 Minor_Axis_C = Major_Axis_b # 3. 遍历轮廓上的点 Minor_Axis_Under_length = 0 Minor_Axis_Above_length = 0 Minor_Axis_Under_End_x = 0 Minor_Axis_Under_End_y = 0 Minor_Axis_Above_End_x = 0 Minor_Axis_Above_End_y = 0 # 轮廓点集 ContourItems = maxContour.shape[0] for item in range(ContourItems): point_x = maxContour[item][0][0] point_y = maxContour[item][0][1] # 判断点在直线哪一侧 # 上侧 if point_y > int(Major_Axis_k * point_x + Major_Axis_b): # 点到直线距离 dis = abs((Minor_Axis_A * point_x + Minor_Axis_B * point_y + Minor_Axis_C) / math.sqrt( Minor_Axis_A * Minor_Axis_A + Minor_Axis_B * Minor_Axis_B)) if dis >= Minor_Axis_Above_length: Minor_Axis_Above_length = dis Minor_Axis_Above_End_x = point_x Minor_Axis_Above_End_y = point_y # 下侧 elif point_y < int(Major_Axis_k * point_x + Major_Axis_b): # 点到直线距离 dis = abs((Minor_Axis_A * point_x + Minor_Axis_B * point_y + Minor_Axis_C) / math.sqrt( Minor_Axis_A * Minor_Axis_A + Minor_Axis_B * Minor_Axis_B)) if dis >= Minor_Axis_Under_length: Minor_Axis_Under_length = dis Minor_Axis_Under_End_x = point_x Minor_Axis_Under_End_y = point_y # 第三种可能就是轮廓与直线的交点 # # 标记两个点,可以忽略 cv2.circle(origin, (Minor_Axis_Above_End_x, Minor_Axis_Above_End_y), 4, (255, 255, 255), -1) cv2.circle(origin, (Minor_Axis_Under_End_x, Minor_Axis_Under_End_y), 4, (255, 255, 255), -1) # 画出两点直线 cv2.line(origin, (Minor_Axis_Under_End_x, Minor_Axis_Under_End_y), (Minor_Axis_Above_End_x, Minor_Axis_Above_End_y), (0, 255, 255), 3) # 计算实际短轴长度 Real_Minor_Axis_Length = math.sqrt( math.pow((Minor_Axis_Above_End_x - Minor_Axis_Under_End_x), 2) + math.pow( (Minor_Axis_Above_End_y - Minor_Axis_Under_End_y), 2)) Real_Minor_Axis_Length = round(Real_Minor_Axis_Length, 1) print "短轴长度 = " + str(Real_Minor_Axis_Length) # 特征二：延长度的计算 P_extend = Real_Major_Axis_Length * 1.0 / Real_Minor_Axis_Length P_extend = round(P_extend, 3) print "延长度 = " + str(P_extend) # 画出与长轴距离最远的两点的辅助线,使用时可以不用，画图用作论文使用 # 画出长轴右方 line_above_k = math.tan((Major_Axis_Angle - 90) * 3.14 / 180.0) line_above_b = Minor_Axis_Above_End_y - line_above_k * Minor_Axis_Above_End_x Minor_Axis_Above_Begin_x = int((line_above_b - Major_Axis_b) / (Major_Axis_k - line_above_k)) Minor_Axis_Above_Begin_y = int(line_above_k * Minor_Axis_Above_Begin_x + line_above_b) cv2.line(origin, (Minor_Axis_Above_Begin_x, Minor_Axis_Above_Begin_y), (Minor_Axis_Above_End_x, Minor_Axis_Above_End_y), (255, 0, 255), 3) line_under_k = math.tan((Major_Axis_Angle - 90) * 3.14 / 180.0) line_under_b = Minor_Axis_Under_End_y - line_under_k * Minor_Axis_Under_End_x Minor_Axis_Under_Begin_x = int((line_under_b - Major_Axi