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 img_only_color(self, filename, oldimg, img_contours):
"""
:param filename: 图像文件
:param oldimg: 原图像文件
:return: 已经定位好的车牌
"""
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])
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)
output = cv2.bitwise_and(hsv, hsv, mask=mask_blue + mask_yellow)
# 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)
debug.img_show(img_edge2)
card_contours = img_math.img_findContours(img_edge2, oldimg)
card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width,
pic_hight)
colors, car_imgs = img_math.img_color(card_imgs)
def img_Transform(car_contours, oldimg, pic_width, pic_hight):
car_imgs = []
for car_rect in car_contours:
if car_rect[2] > -1 and car_rect[2] < 1:
angle = 1
# 对于角度为-1 1之间时，默认为1
else:
angle = car_rect[2]
car_rect = (car_rect[0], (car_rect[1][0] + 5, car_rect[1][1] + 5),
angle)
box = cv2.boxPoints(car_rect)
heigth_point = right_point = [0, 0]
left_point = low_point = [pic_width, pic_hight]
for point in box:
if left_point[0] > point[0]:
left_point = point
if low_point[1] > point[1]:
low_point = point
if heigth_point[1] < point[1]:
heigth_point = point
if right_point[0] < point[0]:
right_point = point
if left_point[1] <= right_point[1]: # 正角度
new_right_point = [right_point[0], heigth_point[1]]
pts2 = np.float32([left_point, heigth_point,
new_right_point]) # 字符只是高度需要改变
pts1 = np.float32([left_point, heigth_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(new_right_point)
point_limit(heigth_point)
point_limit(left_point)
car_img = dst[int(left_point[1]):int(heigth_point[1]),
int(left_point[0]):int(new_right_point[0])]
car_imgs.append(car_img)
debug.img_show(car_img)
elif left_point[1] > right_point[1]: # 负角度
new_left_point = [left_point[0], heigth_point[1]]
pts2 = np.float32([new_left_point, heigth_point,
right_point]) # 字符只是高度需要改变
pts1 = np.float32([left_point, heigth_point, right_point])
M = cv2.getAffineTransform(pts1, pts2)
dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
point_limit(right_point)
point_limit(heigth_point)
point_limit(new_left_point)
car_img = dst[int(right_point[1]):int(heigth_point[1]),
int(new_left_point[0]):int(right_point[0])]
car_imgs.append(car_img)
debug.img_show(car_img)
return car_imgs
def img_findContours(img_contours, oldimg):
img, contours, hierarchy = cv2.findContours(img_contours, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
#contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]
print("findContours len = ", len(contours))
# 排除面积最小的点
debug.img_show(img)
car_contours = []
for cnt in contours:
ant = cv2.minAreaRect(cnt)
width, height = ant[1]
if width < height:
width, height = height, width
ration = width / height
print(ration)
if ration > 2 and ration < 5.5:
car_contours.append(ant)
box = cv2.boxPoints(ant)
box = np.int0(box)
debug.img_contours(oldimg, box)
return car_contours
def show_img_pre(self):
filename = config.get_name()
if filename.any() == True:
debug.img_show(filename)
