def getVideo(window_name, camera_id, catch_pic_num, path_name):
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
model = Model()
model.load_model(model_path)
# 人脸识别分类器
classfier = \
cv2.CascadeClassifier("/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt2.xml")
# 识别出人脸后要画的边框的颜色,RGB格式
color = (0, 255, 0)
# open video
capture = cv2.VideoCapture(camera_id)
captureNum = 0
if not os.path.exists("%s/" % path_name):
os.makedirs("%s/" % path_name)
while capture.isOpened():
ok, img = capture.read()
# 转为灰度，容易识别
grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faceRects = classfier.detectMultiScale(grey,
scaleFactor=1.2,
minNeighbors=3,
minSize=(32, 32))
if len(faceRects) > 0:
for rect in faceRects:
x, y, w, h, = rect
img_name = '%s/me_%d.jpg' % (path_name, captureNum)
image = img[y - 10:y + h + 10, x - 10:x + w + 10]
#cv2.imwrite(img_name, image)
faceId = model.face_predict(image)
print faceId
captureNum += 1
if captureNum > catch_pic_num:
break
cv2.rectangle(img, (x - 10, y - 10), (x + w + 10, y + h + 10),
color, 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, 'num:%d' % (captureNum), (x + 30, y + 30),
font, 1, (255, 0, 255), 4)
if captureNum > catch_pic_num:
break
cv2.imshow(window_name, img)
key = cv2.waitKey(1)
if key == 27:
break
capture.release()
cv2.destroyAllWindows()
def testBadTypeFeatureKeys(self):
with self.assertRaises(Exception) as context:
model = Model(feature_keys_spec='comment_text',
prediction_keys='prediction_key',
model_names=None,
project_name=None)
self.assertIn('Spec should be a dictionary',
str(context.exception))
def testModelIsCompatibleWithDataset(self):
model = Model(feature_keys_spec={
'comment_text': EncodingFeatureSpec.LIST_STRING
},
prediction_keys='prediction_key',
model_names=None,
project_name=None)
def input_fn(max_n_examples):
return pd.DataFrame({
'comment_text': ['This is one'] * max_n_examples,
'label_name': [0] * max_n_examples
})
try:
dataset = Dataset(input_fn)
dataset.check_compatibility(model)
except ValueError:
self.fail('Dataset raised an exception unexpectedly!')
def testInputFnMissingFeatureKeys(self):
model = Model(feature_keys_spec={
'comment_text': EncodingFeatureSpec.LIST_STRING
},
prediction_keys='prediction_key',
model_names=None,
project_name=None)
def input_fn(max_n_examples):
return pd.DataFrame({
'other_feature': ['This is one'] * max_n_examples,
'label_name': [0] * max_n_examples
})
with self.assertRaises(Exception) as context:
dataset = Dataset(input_fn)
dataset.check_compatibility(model)
self.assertIn('input_fn must contain at least the feature keys',
str(context.exception))
def setUp(self):
def input_fn_test(max_n_examples):
return pd.DataFrame(
{'comment_text': [['This', 'is', 'one']] * max_n_examples})
gcs_path_test = os.path.join('gs://kaggle-model-experiments/',
getpass.getuser(), 'unittest',
'dataset_test',
TestEndPipeline.test_version)
self.dataset = Dataset(input_fn_test, gcs_path_test)
self.dataset.load_data(5)
model_input_spec = {
'comment_text': EncodingFeatureSpec.LIST_STRING,
}
self.model = Model(feature_keys_spec=model_input_spec,
prediction_keys='frac_neg/logistic',
example_key='comment_key',
model_names=[
'tf_gru_attention:v_20180914_163804',
'tf_gru_attention:v_20180823_133625'
],
project_name='wikidetox')