mirror of
https://github.com/ccxiao5/Traffic_sign_recognition.git
synced 2025-09-15 12:58:35 +08:00
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
import os
|
||
import numpy as np
|
||
import cv2
|
||
from skimage import feature as ft
|
||
from sklearn.externals import joblib
|
||
|
||
cls_names = ["Straight", "Turn left", "Turn right", "stop", "background"]
|
||
img_label = {"Straight": 0, "Turn left": 1, "Turn right": 2, "stop": 3, "background": 4}
|
||
'''
|
||
通过颜色阈值分割选出蓝色和红色对应的区域得到二值化图像。
|
||
'''
|
||
def preprocess_img(imgBGR):
|
||
##将图像由RGB模型转化成HSV模型
|
||
imgHSV = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2HSV)
|
||
Bmin = np.array([110, 43, 46])
|
||
Bmax = np.array([124, 255, 255])
|
||
##使用inrange(HSV,lower,upper)设置阈值去除背景颜色
|
||
img_Bbin = cv2.inRange(imgHSV,Bmin, Bmax)
|
||
Rmin2 = np.array([165, 43, 46])
|
||
Rmax2 = np.array([180, 255, 255])
|
||
img_Rbin = cv2.inRange(imgHSV,Rmin2, Rmax2)
|
||
img_bin = np.maximum(img_Bbin, img_Rbin)
|
||
return img_bin
|
||
|
||
'''
|
||
提取轮廓,返回轮廓矩形框
|
||
'''
|
||
def contour_detect(img_bin, min_area=0, max_area=-1, wh_ratio=2.0):
|
||
rects = []
|
||
##检测轮廓，其中cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_NONE 存储所有的边界点
|
||
##findContours返回三个值:第一个值返回img，第二个值返回轮廓信息，第三个返回相应轮廓的关系
|
||
contours, hierarchy= cv2.findContours(img_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
||
if len(contours) == 0:
|
||
return rects
|
||
max_area = img_bin.shape[0]*img_bin.shape[1] if max_area<0 else max_area
|
||
for contour in contours:
|
||
area = cv2.contourArea(contour)
|
||
if area >= min_area and area <= max_area:
|
||
x, y, w, h = cv2.boundingRect(contour)
|
||
if 1.0*w/h < wh_ratio and 1.0*h/w < wh_ratio:
|
||
rects.append([x,y,w,h])
|
||
return rects
|
||
'''
|
||
返回带有矩形框的img
|
||
'''
|
||
def draw_rects_on_img(img, rects):
|
||
img_copy = img.copy()
|
||
for rect in rects:
|
||
x, y, w, h = rect
|
||
cv2.rectangle(img_copy, (x,y), (x+w,y+h), (0,255,0), 2)
|
||
return img_copy
|
||
|
||
def hog_extra_and_svm_class(proposal, clf, resize = (64, 64)):
|
||
##对图片进行分类
|
||
img = cv2.cvtColor(proposal, cv2.COLOR_BGR2GRAY)
|
||
img = cv2.resize(img, resize)
|
||
bins = 9
|
||
cell_size = (8, 8)
|
||
cpb = (2, 2)
|
||
norm = "L2"
|
||
features = ft.hog(img, orientations=bins, pixels_per_cell=cell_size,
|
||
cells_per_block=cpb, block_norm=norm, transform_sqrt=True)
|
||
features = np.reshape(features, (1,-1))
|
||
cls_prop = clf.predict_proba(features)
|
||
cls_prop = cls_prop[0]
|
||
return cls_prop
|
||
'''
|
||
视频识别
|
||
'''
|
||
if __name__ == "__main__":
|
||
cap = cv2.VideoCapture(0)
|
||
cv2.namedWindow('camera')
|
||
cv2.resizeWindow("camera",640,480)
|
||
cols = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||
rows = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||
clf = joblib.load("/home/xiao5/Desktop/Traffic_sign_recognition/svm_model.pkl")
|
||
i=0
|
||
while (1):
|
||
i+=1
|
||
ret, img = cap.read()
|
||
img_bin = preprocess_img(img)
|
||
min_area = img_bin.shape[0]*img.shape[1]/(25*25)
|
||
rects = contour_detect(img_bin, min_area=min_area)
|
||
if rects:
|
||
Max_X=0
|
||
Max_Y=0
|
||
Max_W=0
|
||
Max_H=0
|
||
for r in rects:
|
||
if r[2]*r[3]>=Max_W*Max_H:
|
||
Max_X,Max_Y,Max_W,Max_H=r
|
||
proposal = img[Max_Y:(Max_Y+Max_H),Max_X:(Max_X+Max_W)]##用Numpy数组对图像像素进行访问时，应该先写图像高度所对应的坐标(y,row)，再写图像宽度对应的坐标(x,col)。
|
||
cv2.rectangle(img,(Max_X,Max_Y), (Max_X+Max_W,Max_Y+Max_H), (0,255,0), 2)
|
||
cv2.imshow("proposal", proposal)
|
||
cls_prop = hog_extra_and_svm_class(proposal, clf)
|
||
cls_prop = np.round(cls_prop, 2)
|
||
cls_num = np.argmax(cls_prop)##找到最大相似度的索引
|
||
if cls_names[cls_num] is not "background":
|
||
print(cls_names[cls_num])
|
||
else:
|
||
print("N/A")
|
||
cv2.imshow('camera',img)
|
||
cv2.waitKey(40)
|
||
cv2.destroyAllWindows()
|
||
cap.release() |