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 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 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()
try:
Plate = HyperLPR_PlateRecogntion(img_bgr)
# print(Plate[0][0])
r_c = Plate[0][0]
r_color = Plate[0][0]
except:
pass
if not color_color:
color_color = color_c
if not color_c:
color_c = color_color
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_ctl2(pic_path)
return
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_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)
def clean(self):
if self.thread_run:
self.cameraflag = 0
return
self.thread_run = False
self.thread_run2 = False
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')