def from_pic(self):
self.thread_run = False
self.pic_path = askopenfilename(title="选择识别图片", filetypes=[("jpg图片", "*.jpg"), ("png图片", "*.png"),("jpeg图片","*.jpeg")])
if self.pic_path:
img_bgr = img_math.img_read(self.pic_path)
# 图片读取
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
# 图片第一次预处理
self.imgtk = self.get_imgtk(img_bgr)
# 得到能显示图片
self.image_ctl.configure(image=self.imgtk)
# 显示
th1 = ThreadWithReturnValue(target=self.predictor.img_color_contours, args=(first_img, oldimg))
th2 = ThreadWithReturnValue(target=self.predictor.img_only_color, args=(oldimg, oldimg, first_img))
th1.start()
th2.start()
r_c, roi_c, color_c = th1.join()
# 连接成新的字符串
self.show_roi1(r_c, roi_c, color_c)
# r_c 车牌 roi_c 车牌图片 color——c 颜色
print('-----------------------------------------')
print(r_c,' ',roi_c,' ',color_c)
r_color, roi_color, color_color = th2.join()
print(r_color,roi_color,color_color)
self.show_roi2(r_color, roi_color, color_color)
def img_mser(self, filename):
if type(filename) == type(""):
img = img_math.img_read(filename)
else:
img = filename
oldimg = img
mser = cv2.MSER_create(_min_area=600)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
regions, boxes = mser.detectRegions(gray)
colors_img = []
for box in boxes:
x, y, w, h = box
width, height = w, h
if width < height:
width, height = height, width
ration = width / height
if w * h > 1500 and 3 < ration < 4 and w > h:
cropimg = img[y:y + h, x:x + w]
colors_img.append(cropimg)
debug.img_show(img)
colors, car_imgs = img_math.img_color(colors_img)
for i, color in enumerate(colors):
if color != "no":
print(color)
debug.img_show(car_imgs[i])
def isdark(self, car_pic):
if type(car_pic) == type(""):
img = img_math.img_read(car_pic)
else:
img = car_pic
#把图片转换成为灰度图
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#获取灰度图矩阵的行数和列数
rows, cols = gray_img.shape[:2]
dark_sum = 0
dark_prop = 0
piexs_sum = rows * cols
#遍历灰度图的所有像素
for row in gray_img:
for col in row:
if col < 40:
dark_sum += 1
dark_prop = dark_sum / (piexs_sum)
print("总的黑色像素为" + str(dark_sum))
print("总像素是：" + str(piexs_sum))
if dark_prop >= 0.75:
return True
return False
def pic(self, pic_path):
self.apistr = None
img_bgr = img_math.img_read(pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
if not self.cameraflag:
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
th1 = ThreadWithReturnValue(target=self.predictor.img_color_contours,
args=(first_img, oldimg))
th2 = ThreadWithReturnValue(target=self.predictor.img_only_color,
args=(oldimg, oldimg, first_img))
th1.start()
th2.start()
r_c, roi_c, color_c = th1.join()
r_color, roi_color, color_color = th2.join()
self.show_roi2(r_color, roi_color, color_color)
self.show_roi1(r_c, roi_c, color_c)
# self.center_window()
localtime = time.asctime(time.localtime(time.time()))
if not self.cameraflag:
if not (r_color or color_color or r_c or color_c):
self.api_ctl()
value = [
localtime, color_c, r_c, color_color, r_color, self.apistr,
self.pic_source
]
img_excel.excel_add(value)
img_sql.sql(value[0], value[1], value[2], value[3], value[4],
value[5], value[6])
print(localtime, "|", color_c, r_c, "|", color_color, r_color, "| ",
self.apistr, "|", self.pic_source)
def img_first_pre(self, car_pic_file):
"""
:param car_pic_file: 图像文件
:return:已经处理好的图像文件 原图像文件
"""
if type(car_pic_file) == type(""):
img = img_math.img_read(car_pic_file)
else:
img = car_pic_file
pic_hight, pic_width = img.shape[:2]
if pic_width > MAX_WIDTH:
resize_rate = MAX_WIDTH / pic_width
img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA)
# 缩小图片
blur = 5
img = cv2.GaussianBlur(img, (blur, blur), 0)
oldimg = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 转化成灰度图像
Matrix = np.ones((20, 20), np.uint8)
img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, Matrix)
img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0)
# 创建20*20的元素为1的矩阵 开操作，并和img重合
ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_edge = cv2.Canny(img_thresh, 100, 200)
# Otsu’s二值化 找到图像边缘
Matrix = np.ones((4, 19), np.uint8)
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, Matrix)
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
return img_edge2, oldimg
def pic(self, pic_path):
img_bgr = img_math.img_read(pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
if not self.cameraflag:
self.imgtk = self.get_imgtk(img_bgr)
r_color, roi_color, color_color = self.predictor.img_only_color(
oldimg, oldimg, first_img)
print("|", color_color, r_color, "|")
self.car.append(color_color + ' ' + r_color)
return color_color, r_color
def clean(self):
img_bgr3 = img_math.img_read("pic/hy.png")
self.imgtk2 = self.get_imgtk(img_bgr3)
self.image_ctl.configure(image=self.imgtk2)
self.r_ct2.configure(text="")
self.color_ct2.configure(text="", state='enable')
#显示车牌颜色
self.color_ct2.configure(background='white', text="颜色", state='enable')
self.pilImage3 = Image.open("pic/locate.png")
pil_image_resized = self.pilImage3.resize((200, 50), Image.ANTIALIAS)
self.tkImage3 = ImageTk.PhotoImage(image=pil_image_resized)
self.roi_ct2.configure(image=self.tkImage3, state='enable')
def show_img_pre(self):
if self.thread_run:
return
self.thread_run = False
self.thread_run2 = False
filename = img_math.img_read("tmp/img_contours.jpg")
screenwidth = win.winfo_screenwidth()
screenheight = win.winfo_screenheight()
win.update()
width = win.winfo_width()
height = win.winfo_height()
laji1 = int((screenwidth - width) / 2)
laji2 = int((screenheight - height) / 2)
cv2.imshow("preimg", filename)
cv2.moveWindow("preimg", laji1 + 100, laji2)
def videoInsert(self):
print('调用了')
capturePath = "E:/成长ing/计算机编程/Github_code/Python_SMP_OPENCV/capture/zz.jpg"
self.pic_path = capturePath
print(self.pic_path)
if self.pic_path:
img_bgr = img_math.img_read(self.pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
th1 = ThreadWithReturnValue(
target=self.predictor.img_color_contours,
args=(first_img, oldimg))
th2 = ThreadWithReturnValue(target=self.predictor.img_only_color,
args=(oldimg, oldimg, first_img))
th1.start()
th2.start()
r_c, roi_c, color_c = th1.join()
r_color, roi_color, color_color = th2.join()
print('r_c =', r_c, 'r_color =', r_color, 'r_c Node =',
r_c is None)
# 连接服务器操作
connection = pymysql.connect(
host='152.136.105.72', # 服务器地址
user='******', # 数据库账号
password='******', # 数据库密码
db='tcc', # 使用的是tcc这个表
charset='utf8mb4')
try:
with connection.cursor() as cursor:
if (r_c is not None and len(r_c) != 0):
# 创建sql语句
sql = "insert into `tcc_tbl` (`tcc_title`, `tcc_author`, `tcc_indata`, `flag`) values (%s, %s, %s, %s)"
# 执行sql语句
inDate = time.localtime(time.time())
inDateTime = time.strftime('%Y-%m-%d %H:%M:%S',
inDate) # 停车时间
cursor.execute(
sql, ("".join(r_c), "测试author", inDateTime, "2"))
# 提交
connection.commit()
print('插入成功!', "".join(r_c))
finally:
connection.close()
def from_pic(self):
self.thread_run = False
self.pic_path = askopenfilename(title="选择识别图片",
filetypes=[("jpg图片", "*.jpg"),
("png图片", "*.png")])
if self.pic_path:
img_bgr = img_math.img_read(self.pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
th1 = ThreadWithReturnValue(
target=self.predictor.img_color_contours,
args=(first_img, oldimg))
th1.start()
r_c, roi_c, color_c = th1.join()
self.show_roi1(r_c, roi_c, color_c)
def from_pic(self):
self.thread_run = False
self.pic_path = askopenfilename(title="选择识别图片",
filetypes=[("jpg图片", "*.jpg"),
("png图片", "*.png")])
print('self.pic_path =', self.pic_path)
if self.pic_path:
img_bgr = img_math.img_read(self.pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
th1 = ThreadWithReturnValue(
target=self.predictor.img_color_contours,
args=(first_img, oldimg))
th2 = ThreadWithReturnValue(target=self.predictor.img_only_color,
args=(oldimg, oldimg, first_img))
th1.start()
th2.start()
r_c, roi_c, color_c = th1.join()
r_color, roi_color, color_color = th2.join()
# 连接服务器操作
connection = pymysql.connect(
host='152.136.105.72', # 服务器地址
user='******', # 数据库账号
password='******', # 数据库密码
db='tcc', # 使用的是tcc这个表
charset='utf8mb4')
try:
with connection.cursor() as cursor:
# 创建sql语句
sql = "insert into `tcc_tbl` (`tcc_title`, `tcc_author`, `tcc_indata`, `flag`) values (%s, %s, %s, %s)"
# 执行sql语句
tmp = time.localtime(time.time())
dateTime = time.strftime('%Y-%m-%d %H:%M:%S', tmp)
cursor.execute(sql,
("".join(r_c), "测试author", dateTime, '2'))
# 提交
connection.commit()
print('插入成功!')
finally:
connection.close()
self.show_roi2(r_color, roi_color, color_color)
self.show_roi1(r_c, roi_c, color_c)
def pic(self, pic_path):
img_bgr = img_math.img_read(pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
if not self.cameraflag:
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
r_color, roi_color, color_color = self.predictor.img_only_color(
oldimg, oldimg, first_img)
self.color_ct2.configure(background=color_color)
try:
Plate = HyperLPR_PlateRecogntion(img_bgr)
r_color = Plate[0][0]
except:
pass
self.show_roi(r_color, roi_color, color_color)
self.colorimg = color_color
print("|", color_color, r_color, "|", self.pic_source)
def img_first_pre(self, car_pic_file):
"""
:param car_pic_file: 图像文件
:return:已经处理好的图像文件 原图像文件
"""
if type(car_pic_file) == type(""):
img = img_math.img_read(car_pic_file)
else:
img = car_pic_file
pic_hight, pic_width = img.shape[:2]
if pic_width > MAX_WIDTH:
resize_rate = MAX_WIDTH / pic_width
img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)),
interpolation=cv2.INTER_AREA)
# cv_show('缩小的图片', img)
blur = 3
img = cv2.GaussianBlur(img, (blur, blur), 0)
# cv_show('Gauss', img)
oldimg = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv_show('BGR2GRAY', img)
Matrix = np.ones((20, 20), np.uint8)
# 根据给定类型返回一个矩阵
img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, Matrix)
# cv_show('open', img_opening)
img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0)
# cv_show('重叠', img_opening)
ret, img_thresh = cv2.threshold(img_opening, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# cv_show("二值化",img_thresh);
img_edge = cv2.Canny(img_thresh, 100, 200)
# cv_show('边缘检测', img_edge)
Matrix = np.ones((4, 19), np.uint8)
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, Matrix)
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
# cv_show('形态学操作', img_edge2)
return img_edge2, oldimg
def clean(self):
if self.thread_run:
self.cameraflag = 0
return
self.thread_run = False
self.thread_run2 = False
# self.center_window()
self.p1.set("")
img_bgr3 = img_math.img_read("pic/hy.png")
self.imgtk2 = self.get_imgtk(img_bgr3)
self.image_ctl.configure(image=self.imgtk2)
self.r_ctl.configure(text="")
self.color_ctl.configure(text="", state='enable')
self.r_ct2.configure(text="")
self.color_ct2.configure(text="", state='enable')
self.pilImage3 = Image.open("pic/locate.png")
w, h = self.pilImage3.size
pil_image_resized = self.resize(w, h, self.pilImage3)
self.tkImage3 = ImageTk.PhotoImage(image=pil_image_resized)
self.roi_ctl.configure(image=self.tkImage3, state='enable')
self.roi_ct2.configure(image=self.tkImage3, state='enable')
def ysdingwei():
lable4 = tk.Label(window, text="颜色定位如下", font=('Arial', 15))
lable4.place(x=750, y=100)
filename = img_math.img_read(pic_path)
oldimg = filename
img_contours = oldimg
pic_hight, pic_width = img_contours.shape[:2]
lower_blue = np.array([100, 110, 110])
upper_blue = np.array([130, 255, 255])
lower_yellow = np.array([15, 55, 55])
upper_yellow = np.array([50, 255, 255])
lower_green = np.array([50, 50, 50])
upper_green = np.array([100, 255, 255])
hsv = cv2.cvtColor(filename, cv2.COLOR_BGR2HSV)
mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
mask_green = cv2.inRange(hsv, lower_yellow, upper_green)
output = cv2.bitwise_and(hsv, hsv, mask=mask_blue + mask_yellow + mask_green)
# 根据阈值找到对应颜色
output = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
Matrix = np.ones((20, 20), np.uint8)
img_edge1 = cv2.morphologyEx(output, cv2.MORPH_CLOSE, Matrix)
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
card_contours = img_math.img_findContours(img_edge2)
card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width, pic_hight)
colors, car_imgs = img_math.img_color(card_imgs)
cv2.imwrite('tmp/chepai_img1.jpg', card_imgs[0])
print(colors[0])
img = Image.open("tmp/chepai_img1.jpg")
photo = ImageTk.PhotoImage(img)
img = img.resize((160, 40), Image.ANTIALIAS)
photo = ImageTk.PhotoImage(img)
label2 = tk.Label(window,anchor="ne")
label2.config(image=photo)
label2.image=photo
label2.place(x=735,y=130)
def jiazai(self):
img_1 = img_math.img_read("tmp/img_gray.jpg")
self.img1 = self.get_imgtk(img_1)
self.image_1.configure(image=self.img1)
img_2 = img_math.img_read("tmp/img_edge.jpg")
self.img2 = self.get_imgtk(img_2)
self.image_2.configure(image=self.img2)
img_3 = img_math.img_read("tmp/img_xingtai.jpg")
self.img3 = self.get_imgtk(img_3)
self.image_3.configure(image=self.img3)
img_4 = img_math.img_read("tmp/img_caijian.jpg")
self.img4 = self.get_imgtk(img_4)
self.image_4.configure(image=self.img4)
img_5_1 = img_math.img_read("tmp/chechar1.jpg")
self.img51 = self.get_imgtk_1(img_5_1)
self.image_5_1.configure(image=self.img51)
img_5_2 = img_math.img_read("tmp/chechar2.jpg")
self.img52 = self.get_imgtk_1(img_5_2)
self.image_5_2.configure(image=self.img52)
img_5_3 = img_math.img_read("tmp/chechar3.jpg")
self.img53 = self.get_imgtk_1(img_5_3)
self.image_5_3.configure(image=self.img53)
img_5_4 = img_math.img_read("tmp/chechar4.jpg")
self.img54 = self.get_imgtk_1(img_5_4)
self.image_5_4.configure(image=self.img54)
img_5_5 = img_math.img_read("tmp/chechar5.jpg")
self.img55 = self.get_imgtk_1(img_5_5)
self.image_5_5.configure(image=self.img55)
img_5_6 = img_math.img_read("tmp/chechar6.jpg")
self.img56 = self.get_imgtk_1(img_5_6)
self.image_5_6.configure(image=self.img56)
img_5_7 = img_math.img_read("tmp/chechar7.jpg")
self.img57 = self.get_imgtk_1(img_5_7)
self.image_5_7.configure(image=self.img57)
def insertDate(self):
file = "C:/Users/fuchen/Desktop/good_park_test"
for root, dirs, files in os.walk(file):
print(files) # 当前路径下所有非目录子文件
for name in files:
self.pic_path = file + "/" + name
print(self.pic_path)
if self.pic_path:
img_bgr = img_math.img_read(self.pic_path)
first_img, oldimg = self.predictor.img_first_pre(img_bgr)
self.imgtk = self.get_imgtk(img_bgr)
self.image_ctl.configure(image=self.imgtk)
th1 = ThreadWithReturnValue(
target=self.predictor.img_color_contours,
args=(first_img, oldimg))
th2 = ThreadWithReturnValue(
target=self.predictor.img_only_color,
args=(oldimg, oldimg, first_img))
th1.start()
th2.start()
r_c, roi_c, color_c = th1.join()
r_color, roi_color, color_color = th2.join()
print('r_c =', r_c, 'r_color =', r_color, 'r_c Node =',
r_c is None)
# 连接服务器操作
connection = pymysql.connect(
host='152.136.105.72', # 服务器地址
user='******', # 数据库账号
password='******', # 数据库密码
db='tcc', # 使用的是tcc这个表
charset='utf8mb4')
try:
with connection.cursor() as cursor:
if (r_c is not None and len(r_c) != 0):
# 创建sql语句
sql = "insert into `tcc_tbl` (`tcc_title`, `tcc_author`, `tcc_indata`, `tcc_outdata`, `flag`) values (%s, %s, %s, %s, %s)"
# 执行sql语句
tmp = time.time(
) - random.random() * 60 * 60 * 24 * 30
inDate = time.localtime(tmp)
outDate = time.localtime(tmp + 60 * 60 * 3)
inDateTime = time.strftime(
'%Y-%m-%d %H:%M:%S', inDate)
outDateTime = time.strftime(
'%Y-%m-%d %H:%M:%S', outDate)
if (random.random() > 0.4):
cursor.execute(
sql, ("".join(r_c), "测试author",
inDateTime, outDateTime, "1"))
else:
cursor.execute(sql,
("".join(r_c), "测试author",
inDateTime, "", "2"))
# 提交
connection.commit()
print('插入成功!', "".join(r_c))
finally:
connection.close()
def preprocess(self, car_pic_file):
"""
:param car_pic_file: 图像文件
:return:已经处理好的图像文件 原图像文件
"""
if type(car_pic_file) == type(""):
img = img_math.img_read(car_pic_file)
else:
img = car_pic_file
if img.any() == None:
return
pic_hight, pic_width = img.shape[:2]
print("图片长高为{}，图片长为{}".format(pic_hight, pic_width))
#适当缩小图片
if pic_width > MAX_WIDTH: #如果过大了
resize_rate = MAX_WIDTH / pic_width
img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)),
interpolation=cv2.INTER_AREA)
#img = cv2.equalizeHist(img)
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #转成灰度图
# cv2.imshow("gray", gray_img)
# cv2.waitKey(0)
# dst = cv2.equalizeHist(gray_img)
# cv2.imshow("dst",dst)
# cv2.waitKey(0)
blur_img = cv2.blur(gray_img, (3, 3)) #均值模糊
#blur_img = cv2.medianBlur(gray_img,3)
#cv2.imshow("blur",blur_img)
#cv2.waitKey(0)
sobel_img = cv2.Sobel(blur_img, cv2.CV_16S, 1, 0,
ksize=3) #sobel获取垂直边缘
sobel_img = cv2.convertScaleAbs(sobel_img)
#sobel_img = cv2.Canny(blur_img,100,200)
cv2.imshow("sobel", sobel_img)
cv2.waitKey(0)
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) #转成HSV
# cv2.imshow("hsv",hsv_img)
# cv2.waitKey(0)
h, s, v = hsv_img[:, :, 0], hsv_img[:, :, 1], hsv_img[:, :, 2]
#黄色的色调区间再[26,34],蓝色的色调区间再[100,124]，绿色的色调区间在[35,100]
blue_img = (((h > 15) & (h <= 124))) & (s > 70) & (v > 70) #橙色和紫色
blue_img = blue_img.astype('float32')
mix_img = np.multiply(sobel_img, blue_img)
# cv2.imshow('mix', mix_img)
# cv2.waitKey(0)
mix_img = mix_img.astype(np.uint8)
ret, binary_img = cv2.threshold(mix_img, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# cv2.imshow('binary',binary_img)
# cv2.waitKey(0)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 5))
close_img = cv2.morphologyEx(binary_img, cv2.MORPH_CLOSE, kernel)
##config##
threshold_m = int(pic_width / 80) #根据自己调参出来的
print("开运算的阈值为" + str(threshold_m))
x = threshold_m
y = int(threshold_m * 1.3)
##config##
Matrix = np.ones((x, y), np.uint8)
img_edge1 = cv2.morphologyEx(close_img, cv2.MORPH_CLOSE, Matrix)
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
return img_edge2, img
def img_first_pre(self, car_pic_file):
"""
:param car_pic_file: 图像文件
:return:已经处理好的图像文件 原图像文件
"""
if type(car_pic_file) == type(""):
img = img_math.img_read(car_pic_file) #读取文件
else:
img = car_pic_file
pic_hight, pic_width = img.shape[:2] #取彩色图片的高、宽
if pic_width > MAX_WIDTH:
resize_rate = MAX_WIDTH / pic_width
# 缩小图片
img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)),
interpolation=cv2.INTER_AREA)
# 关于interpolation 有几个参数可以选择:
# cv2.INTER_AREA - 局部像素重采样，适合缩小图片。
# cv2.INTER_CUBIC和 cv2.INTER_LINEAR 更适合放大图像，其中INTER_LINEAR为默认方法。
img = cv2.GaussianBlur(img, (5, 5), 0)
# 高斯滤波是一种线性平滑滤波，对于除去高斯噪声有很好的效果
# 0 是指根据窗口大小（ 5,5 ）来计算高斯函数标准差
oldimg = img
# 转化成灰度图像
# 转换颜色空间 cv2.cvtColor
# BGR ---> Gray cv2.COLOR_BGR2GRAY
# BGR ---> HSV cv2.COLOR_BGR2HSV
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imwrite("tmp/img_gray.jpg", img)
#ones()返回一个全1的n维数组
Matrix = np.ones((20, 20), np.uint8)
# 开运算:先进性腐蚀再进行膨胀就叫做开运算。它被用来去除噪声。 cv2.MORPH_OPEN
img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, Matrix)
# 图片叠加与融合
# g (x) = (1 − α)f0 (x) + αf1 (x) a→（0，1）不同的a值可以实现不同的效果
img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0)
# cv2.imwrite("tmp/img_opening.jpg", img_opening)
# 创建20*20的元素为1的矩阵 开操作，并和img重合
# Otsu’s二值化
ret, img_thresh = cv2.threshold(img_opening, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Canny 边缘检测
# 较大的阈值2用于检测图像中明显的边缘 一般情况下检测的效果不会那么完美，边缘检测出来是断断续续的
# 较小的阈值1用于将这些间断的边缘连接起来
img_edge = cv2.Canny(img_thresh, 100, 200)
cv2.imwrite("tmp/img_edge.jpg", img_edge)
Matrix = np.ones((4, 19), np.uint8)
# 闭运算:先膨胀再腐蚀
img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, Matrix)
# 开运算
img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
cv2.imwrite("tmp/img_xingtai.jpg", img_edge2)
return img_edge2, oldimg
