def test_chars_identify():
'''
字符鉴别
:return:
'''
print("Testing Chars Identify")
file = "resources/image/chars_identify.jpg"
src = cv2.imread(file)
imshow("src", src)
result = []
plate = CharsSegment()
ci = CharsIdentify()
plate_license = []
if plate.charsSegment(src, result) == 0:
plate_license = ci.identify(np.array(result)[..., None])
pred = ""
for index in plate_license:
pred += index2str[index]
print("Plate License: ", "苏E771H6")
print("Plate Identify: ", pred)
if pred == "苏E771H6":
print("Identify Correct")
else:
print("Identify Not Correct")
def test_plate_judge():
'''
车牌判断
:return:
'''
print("Testing Plate Judge")
file = "resources/image/plate_judge.jpg"
src = cv2.imread(file)
result = []
plate = PlateLocate()
plate.setDebug(False)
plate.setLifemode(True)
if plate.plateLocate(src, result) == 0:
for res in result:
imshow("plate judge", res)
judge_result = PlateJudge().judge(np.array(result))
if len(judge_result) != 0:
for res in judge_result:
imshow("plate judge", res)
def test_chars_recognise():
print("Testing Chars Recognise")
file = "resources/image/chars_recognise.jpg"
src = cv2.imread(file)
imshow("src", src)
cr = CharsRecognise()
print("Chars Recognise: ", cr.charsRecognise(src))
def test_char_segment():
print("Testing Chars Segment")
file = "resources/image/chars_segment.jpg"
src = cv2.imread(file)
imshow("src", src)
result = []
plate = CharsSegment()
if plate.charsSegment(src, result) == 0:
for i in range(len(result)):
imshow("plate segment " + str(i), result[i])
def test_plate_detect():
print("Testing Plate Detect")
file = "resources/image/plate_detect.jpg"
src = cv2.imread(file)
result = []
pd = PlateDetect()
pd.setPDLifemode(True)
if pd.plateDetect(src, result) == 0:
for res in result:
imshow("plate detect", res)
def test_plate_locate():
print("Testing Plate Locate")
file = "resources/image/test.jpg"
src = cv2.imread(file)
result = []
plate = PlateLocate()
plate.setDebug(False)
plate.setLifemode(True)
if plate.plateLocate(src, result) == 0:
for res in result:
imshow("plate locate", res)
def test_plate_recognize():
print("Testing Plate Recognize")
file = "resources/image/test.jpg"
src = cv2.imread(file)
imshow("src", src)
result_detection = []
pd = PlateDetect()
pd.setPDLifemode(True)
pd.plateDetect(src, result_detection)
cr = CharsRecognise()
for res in result_detection:
imshow("Plate Recognize", res)
print("Chars Recognise: ", cr.charsRecognise(res))
def test_plate_locate():
'''
车牌定位
:return:
'''
print("Testing Plate Locate")
file = "resources/image/test.jpg"
# file = "/Users/huijunzhang/Downloads/pic/pic2.jpg"
src = cv2.imread(file)
result = []
plate = PlateLocate()
plate.setDebug(False)
plate.setLifemode(True)
if plate.plateLocate(src, result) == 0:
for res in result:
imshow("plate locate", res)
def colorSearch(self, src, color, out_rect):
'''
:param src:
:param color:
:param out_rect: minAreaRect
:return: binary
'''
color_morph_width = 10
color_morph_height = 2
match_gray = colorMatch(src, color, False)
if self.m_debug:
imshow('match_gray_' + str(color), match_gray)
# imwrite("resources/image/tmp/", "match_gray.jpg", match_gray)
_, src_threshold = cv2.threshold(match_gray, 0, 255,
cv2.THRESH_OTSU + cv2.THRESH_BINARY)
element = cv2.getStructuringElement(
cv2.MORPH_RECT, (color_morph_width, color_morph_height))
src_threshold = cv2.morphologyEx(src_threshold, cv2.MORPH_CLOSE,
element)
if self.m_debug:
imshow('color', src_threshold)
# imwrite("resources/image/tmp/", "color.jpg", src_threshold)
out = src_threshold.copy()
_, contours, _ = cv2.findContours(src_threshold, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for cnt in contours:
mr = cv2.minAreaRect(cnt)
if self.verifySizes(mr):
out_rect.append(mr)
return out
images = []
labels = []
for i in range(self.batch_size):
image, label = self.image_label_queue.get()
images.append(image)
labels.append(label)
images = np.asarray(images, dtype=np.float32)
images = images / 255 * 2 - 1
return images, labels
if __name__ == "__main__":
#Test
batch_size = 32
dataset_params = {
'batch_size': batch_size,
'path': '../resources/train_data/chars',
'labels_path': '../resources/train_data/chars_list_train.pickle',
'thread_num': 1
}
train_dataset_reader = DataSet(dataset_params)
images, labels = train_dataset_reader.batch()
imshow('tmp', images[0])
imshow('tmp', images[1])
images, labels = train_dataset_reader.batch()
imshow('tmp', images[0])
imshow('tmp', images[1])
