目录
1.学习目标
2.图像特征理解
2.1图像特征
2.2颜色特征
2.3纹理特征
2.4形状特征
2.5空间关系特征
3.形状特征描述
3.1HOG特征提取
3.2HOG实现过程
3.3角点概念
3.4Harris角点检测
3.5Harris实现过程
3.6SIFT算法
3.7SIFT实现过程
4.LBP纹理特征
4.1LBP介绍
4.2LBP原理
5.模板匹配算法
6.人脸检测算法
6.1人脸检测
6.2人脸对齐
6.3人脸特征提取
6.4人脸识别
第五点补充说明：将0-360度分布的梯度方向映射到bin个小的区间内。
代码：
import cv2 import numpy as np # 判断矩形i是否完全包含在矩形o中 def is_inside(o, i): ox, oy, ow, oh = o ix, iy, iw, ih = i return ox > ix and oy > iy and ox + ow < ix + iw and oy + oh < iy + ih # 对人体绘制颜色框 def draw_person(image, person): x, y, w, h = person cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 255), 2) img = cv2.imread("people.jpg") hog = cv2.HOGDescriptor() # 启动检测器对象 hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) # 指定检测器类型为人体 found, w = hog.detectMultiScale(img,0.1,(1,1)) # 加载并检测图像 print(found) print(w) # 丢弃某些完全被其它矩形包含在内的矩形 found_filtered = [] for ri, r in enumerate(found): for qi, q in enumerate(found): if ri != qi and is_inside(r, q): break else: found_filtered.append(r) print(found_filtered) # 对不包含在内的有效矩形进行颜色框定 for person in found_filtered: draw_person(img, person) cv2.imshow("people detection", img) cv2.waitKey(0) cv2.destroyAllWindows()输出：
[[322 122 92 184] [228 107 94 188] [ 97 109 92 185] [394 132 87 173] [157 143 89 178]] [[1.69752147] [1.50364332] [2.26940114] [0.15458478] [1.37897637]] [array([322, 122, 92, 184], dtype=int32)] [array([322, 122, 92, 184], dtype=int32), array([228, 107, 94, 188], dtype=int32)] [array([322, 122, 92, 184], dtype=int32), array([228, 107, 94, 188], dtype=int32), array([ 97, 109, 92, 185], dtype=int32)] [array([322, 122, 92, 184], dtype=int32), array([228, 107, 94, 188], dtype=int32), array([ 97, 109, 92, 185], dtype=int32), array([394, 132, 87, 173], dtype=int32)] [array([322, 122, 92, 184], dtype=int32), array([228, 107, 94, 188], dtype=int32), array([ 97, 109, 92, 185], dtype=int32), array([394, 132, 87, 173], dtype=int32), array([157, 143, 89, 178], dtype=int32)]代码：
import cv2 import numpy as np filename = 'harris2.png' img = cv2.imread(filename) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gray = np.float32(gray) # 输入图像必须是 float32 ,最后一个参数在 0.04 到 0.06 之间 dst = cv2.cornerHarris(gray,2,3,0.06) #结果进行膨胀，可有可无 dst = cv2.dilate(dst,None) print(dst) # 设定阈值，不同图像阈值不同 img[dst>0.01*dst.max()]=[0,0,255] print(dst.max()) cv2.imshow('dst_img',img) #cv2.imshow('dst',dst) cv2.waitKey(0) cv2.destroyAllWindows()输出：
[[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]] 533091900.0代码：
import cv2 import numpy as np img = cv2.imread('harris2.png') gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) sift = cv2.xfeatures2d.SIFT_create() kp = sift.detect(gray,None)#找到关键点 img=cv2.drawKeypoints(gray,kp,img)#绘制关键点 cv2.imshow('sp',img) cv2.waitKey(0) cv2.destroyAllWindows()输出：
代码：
def LBP(src): ''' :param src:灰度图像 :return: ''' height = src.shape[0] width = src.shape[1] dst = src.copy() lbp_value = np.zeros((1,8), dtype=np.uint8) #print(lbp_value) neighbours = np.zeros((1,8), dtype=np.uint8) #print(neighbours) for x in range(1, width-1): for y in range(1, height-1): neighbours[0, 0] = src[y - 1, x - 1] neighbours[0, 1] = src[y - 1, x] neighbours[0, 2] = src[y - 1, x + 1] neighbours[0, 3] = src[y, x - 1] neighbours[0, 4] = src[y, x + 1] neighbours[0, 5] = src[y + 1, x - 1] neighbours[0, 6] = src[y + 1, x] neighbours[0, 7] = src[y + 1, x + 1] center = src[y, x] for i in range(8): if neighbours[0, i] > center: lbp_value[0, i] = 1 else: lbp_value[0, i] = 0 lbp = lbp_value[0, 0] * 1 + lbp_value[0, 1] * 2 + lbp_value[0, 2] * 4 + lbp_value[0, 3] * 8 \ + lbp_value[0, 4] * 16 + lbp_value[0, 5] * 32 + lbp_value[0, 6] * 64 + lbp_value[0, 7] * 128 #print(lbp) dst[y, x] = lbp return dst import cv2 import numpy as np img = cv2.imread('people.jpg',0) print(img.shape) cv2.imshow('src',img) cv2.waitKey(0) cv2.destroyAllWindows() new_img = LBP(img) cv2.imshow('dst',new_img) cv2.waitKey(0) cv2.destroyAllWindows()输出：
(334, 500)
平方差匹配的方法速度最快，效果无法保证；相关性匹配的方法速度比平方差匹配慢，但效果要好一些；相关性系数匹配速度最慢，效果最好。
代码：
#模板匹配 import cv2 import numpy as np def template_demo(tpl,target): methods = [cv2.TM_SQDIFF_NORMED, cv2.TM_CCORR_NORMED, cv2.TM_CCOEFF_NORMED] #3种模板匹配方法 th, tw = tpl.shape[:2] for md in methods: #print(md) result = cv2.matchTemplate(target, tpl, md) #print(result.shape) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) print(min_val, max_val, min_loc, max_loc) if md == cv2.TM_SQDIFF_NORMED: tl = min_loc else: tl = max_loc br = (tl[0]+tw, tl[1]+th) #br是矩形右下角的点的坐标 cv2.rectangle(target, tl, br, (0, 0, 255), 2) cv2.namedWindow("match-" + np.str(md), cv2.WINDOW_NORMAL) cv2.imshow("match-" + np.str(md), target) tpl =cv2.imread("sample2.jpg") print(tpl.shape) target = cv2.imread("target1.jpg") print(target.shape) cv2.waitKey(0) cv2.destroyAllWindows() cv2.namedWindow('template image', cv2.WINDOW_NORMAL) cv2.imshow("template image", tpl) cv2.namedWindow('target image', cv2.WINDOW_NORMAL) cv2.imshow("target image", target) template_demo(tpl,target) cv2.waitKey(0) cv2.destroyAllWindows()输出：
代码1：
import cv2 # 读入图像 img = cv2.imread("12.jpg") img = cv2.resize(img,None,fx=0.4,fy=0.4,interpolation=cv2.INTER_LINEAR) # 加载人脸特征，该文件在 python安装目录\Lib\site-packages\cv2\data 下 face_cascade = cv2.CascadeClassifier(r'haarcascade_frontalface_default.xml') # 将读取的图像转为COLOR_BGR2GRAY，减少计算强度 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 检测出的人脸个数 faces = face_cascade.detectMultiScale(gray, scaleFactor = 1.15, minNeighbors = 4, minSize = (5, 5)) print("Face : {0}".format(len(faces))) print(faces) # 用矩形圈出人脸的位置 for(x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.namedWindow("Faces") cv2.imshow("Faces", img) cv2.waitKey(0) cv2.destroyAllWindows()输出：
Face : 5 [[128 110 48 48] [188 107 50 50] [338 81 54 54] [260 93 53 53] [ 55 106 44 44]]PS：毕业非正式照，哈哈哈哈
代码2：
# -*- coding：utf-8 -*- import cv2 import dlib import numpy as np predictor_model = 'shape_predictor_68_face_landmarks/shape_predictor_68_face_landmarks.dat' detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_model) # cv2读取图像 test_film_path = "3.png" img = cv2.imread(test_film_path) # 取灰度 img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) # 人脸数rects rects = detector(img_gray, 0) print(rects[0]) for i in range(len(rects)): landmarks = np.matrix([[p.x, p.y] for p in predictor(img,rects[i]).parts()]) print(landmarks, type(landmarks)) for idx, point in enumerate(landmarks): # 68点的坐标 pos = (point[0, 0], point[0, 1]) #print(idx+1, pos) # 利用cv2.circle给每个特征点画一个圈，共68个 cv2.circle(img, pos, 3, color=(0, 255, 0)) # 利用cv2.putText输出1-68 font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(img, str(idx+1), pos, font, 0.5, (0, 0, 255), 1, cv2.LINE_AA) #cv2.imwrite("result.png", img) cv2.imshow("img", img) cv2.waitKey(0) cv2.destroyAllWindows()输出：
