案例@目标方向侦测.zip

import cv2 from sklearn.datasets import load_iris import numpy as np import matplotlib.pyplot as plt from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas import math class PCAIris: def __init__(self): # 加载iris数据集, 不需要标签 self.data, _ = load_iris(return_X_y=True) # 取两个特征，因为我们需要可视化到二位空间， 而且只绘制其中第二类50个样本点 self.data = self.data[50:100,[0,2]] # 绘制的图像 self.img = np.ndarray(shape=(1024, 1024, 3), dtype=np.uint8) self.img.fill(255) # 白色背景 def draw_pca_dir(self, point1, point2, color, scale=0.2): """ point1, point2是线段的两个点， color是线段的颜色 scale是放大因子，根据样本的大小对应放大缩小 线段的箭头，长度为9 """ # 绘制pca的两个主成分方向 # 计算pca线段的方向 angle = math.atan2(float(point1[1] - point2[1]), float(point1[0] - point2[0])) # 计算两个点之间距离（线段长度） distance = math.sqrt(float(point1[1] - point2[1]) ** 2 + float(point1[0] - point2[0]) ** 2) # point1不动，按照scale进行线段放大 new_x = int(point1[0] - scale * distance * math.cos(angle)) new_y = int(point1[1] - scale * distance * math.sin(angle)) self.img = cv2.line(self.img, point1, (new_x, new_y), color, 1, cv2.LINE_AA) # 绘制箭头（以point2为中心点） len_arrow = 10 * 2 # 箭头的第01个线段 arrow1_x = int(new_x + len_arrow * math.cos(angle + math.pi / 4.0)) arrow1_y = int(new_y + len_arrow * math.sin(angle + math.pi / 4.0)) self.img = cv2.line(self.img, (arrow1_x, arrow1_y), (new_x, new_y), color, 1, cv2.LINE_AA) # 箭头的第02个线段 arrow2_x = int(new_x + len_arrow * math.cos(angle - math.pi / 4.0)) arrow2_y = int(new_y + len_arrow * math.sin(angle - math.pi / 4.0)) self.img = cv2.line(self.img, (arrow2_x, arrow2_y), (new_x, new_y), color, 1, cv2.LINE_AA) def draw_samples(self): # 根据图像大小缩放样本 # 获取样本点最大与最小值，用来缩放 max_x, max_y = np.amax(self.data, axis=0) min_x, min_y = np.amin(self.data, axis=0) max_x += 0.5 max_y += 0.5 min_x -= 0.5 min_y -= 0.5 # 图像的大小 h, w, _ = self.img.shape # 深度不考虑 # 计算缩放比例 scale_x = w / (max_x - min_x) scale_y = h / (max_y - min_y) color = (0, 0, 255) # 绘制颜色 # 循环绘制样本点 for x, y in self.data: # 平移到原点放大 x -= min_x y -= min_y x *= scale_x y *= scale_y self.img = cv2.circle(self.img, (int(x), int(y)), 2, color, -1) def draw_pca(self): # 计算样本的最大最小值 max_x, max_y = np.amax(self.data, axis=0) min_x, min_y = np.amin(self.data, axis=0) # 扩边，防止数据点落在边界 max_x += 0.5 max_y += 0.5 min_x -= 0.5 min_y -= 0.5 # 图像的大小 h, w, _ = self.img.shape # 深度不考虑 # 计算缩放比例 scale_x = w / (max_x - min_x) scale_y = h / (max_y - min_y) # 计算中心点 c_x, c_y = np.mean(self.data, axis=0) # 中心点平移 c_x -= min_x c_y -= min_y # 放大 c_x *= scale_x c_y *= scale_y # 绘制中心点 cv2.circle(self.img, (int(c_x), int(c_y)), 5, (255, 0, 0), 2) # 计算PCA特征值与特征向量 e_mean, e_vec, e_val = cv2.PCACompute2(self.data, np.mean(self.data, axis=0).reshape(1, -1)) # 使用特征值与特征向量构造两个坐标轴 p1_x = int(c_x) + int(e_vec[0][0] * e_val[0][0] * scale_x) p1_y = int(c_y) + int(e_vec[0][1] * e_val[0][0] * scale_y) p2_x = int(c_x) + int(e_vec[1][0] * e_val[1][0] * scale_x) p2_y = int(c_y) + int(e_vec[1][1] * e_val[1][0] * scale_y) self.draw_pca_dir((int(c_x), int(c_y)), (p1_x, p1_y), (255, 0, 0), 1.5) self.draw_pca_dir((int(c_x), int(c_y)), (p2_x, p2_y), (255, 0, 0), 5) class PCAApply: def __init__(self, filename): self.img = cv2.imread(filename) # 把图像转换为灰度图，使用轮廓抽取，分成不同目标，每个目标判定方向 def getObjects(self): # 灰度图像 img_gray = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY) # 二值化图像(第一个参数返回优化的阈值) _, img_bin = cv2.threshold(img_gray, 50,255, cv2.THRESH_BINARY | cv2.THRESH_OTSU) # 抽取轮廓（发现物体） contours, _ = cv2.findContours(img_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) # 循环处理轮廓 for i in range(len(contours)): # 获取轮廓的面积，面积过大过小都不绘制 area = cv2.contourArea(contours[i]) if area < 0.001 or area >20000: continue # 绘制轮廓 cv2.drawContours(self.img, contours, i, (0, 0, 255), 3) # 计算每个目标的方向 self.getOrientation(contours[i]) def draw_pca_dir(self, point1, point2, color, scale=0.2): """ point1, point2是线段的两个点， color是线段的颜色 scale是放大因子，根据样本的大小对应放大缩小 线段的箭头，长度为9 """ # 绘制pca的两个主成分方向 # 计算pca线段的方向 angle = math.atan2(float(point1[1] - point2[1]), float(point1[0] - point2[0])) # 计算两个点之间距离（线段长度） distance = math.sqrt(float(point1[1] - point2[1]) ** 2 + float(point1[0] - point2[0]) ** 2) # point1不动，按照scale进行线段放大 new_x = int(point1[0] - scale * distance * math.cos(angle)) new_y = int(point1[1] - scale * distance * math.sin(angle)) self.img = cv2.line(self.img, point1, (new_x, new_y), color, 1, cv2.LINE_AA) # 绘制箭头（以point2为中心点） len_arrow = 10 # 箭头的第01个线段 arrow1_x = int(new_x + len_arrow * math.cos(angle + math.pi / 4.0)) arrow1_y = int(new_y + len_arrow * math.sin(angle + math.pi / 4.0)) self.img = cv2.line(self.img, (arrow1_x, arrow1_y), (new_x, new_y), color, 1, cv2.LINE_AA) # 箭头的第02个线段 arrow2_x = int(new_x + len_arrow * math.cos(angle - math.pi / 4.0)) arrow2_y = int(new_y + len_arrow * math.sin(angle - math.pi / 4.0)) self.img = cv2.line(self.img, (arrow2_x, arrow2_y), (new_x, new_y), color, 1, cv2.LINE_AA) def getOrientation(self, contour): # 先把contour处理成与iris一样的二维数据 data = np.squeeze(contour).astype(np.float) # 下面需要浮点数才能运算 # 计算均值中心点 c_x, c_y = np.mean(data, axis=0) # print(data) # 计算特征值与特征向量 _, e_vec, e_val = cv2.PCACompute2(data, np.mean(data, axis=0).reshape(1, -1), ) # 绘制中心点 self.img = cv2.circle(self.img, (int(c_x), int(c_y)), 3, (0, 0, 255), 2) # 计算坐标轴的另外一个端点 p1_x = int(c_x + e_vec[0][0] * e_val[0][0] * 0.02) # 缩小0.02 p1_y = int(c_y + e_vec[0][1] * e_val[0][0] * 0.02) p2_x = int(c_x + e_vec[1][0] * e_val[1][0] * 0.02) p2_y = int(c_y + e_vec[1][1] * e_val[1][0] * 0.02) # 绘制两�