def test_check_required(self):
"""key必須チェック
"""
data = {"items": [{"sequence": 123}]}
self.assertNotEqual(validate.valid(data), "OK")
data = {"items": [{"item": "coffee"}]}
self.assertNotEqual(validate.valid(data), "OK")
def index():
if not request.method == 'POST':
abort(405)
data = json.loads(request.data.decode('utf-8'))
ret = vl.valid(data)
if not ret == 'OK':
app.logger.error('schema error')
abort(400)
return jsonify({"message": "OK"})
def test_validation_check(self):
"""正常系テスト
"""
data = {
"items": [{
"item": "coffee",
"sequence": 123
}, {
"item": "mocha",
"sequence": 456
}]
}
self.assertEqual(validate.valid(data), "OK")
def detect(rgb):
# for validation
predictList = []
featureList = []
index = 0
rgb = cv2.resize(rgb,(400,300))
gray = cv2.cvtColor(rgb,cv2.COLOR_RGB2GRAY)
#red, yellow segmentation
segIm = image_process.color_segmentation(rgb)
# filtering ROIs
statsListR,roiListR = image_process.get_roi(segIm[0],rgb, 1)
statsListY,roiListY = image_process.get_roi(segIm[1],rgb, 2)
statsList = statsListR + statsListY
roiList = roiListR + roiListY
## HOG feature of shape
if len(roiList) > 0:
for idx, roi in enumerate(roiList):
feature = image_process.hogFeature(roi,(60,60), 9,(8,8), (2,2), 'L2')
if feature is not None:
featureList.append(feature)
# predict shape and sign
if len(featureList) > 0:
featureList = np.float32(featureList)
predictList = svm1.predict(featureList)[1].ravel()
#speed limit sign -> predict number
for idx, predict in enumerate(predictList):
if predict == 1 or predict == 2 or predict == 3:
feat= []
img = np.uint8(gray[statsList[idx][1]:statsList[idx][3], statsList[idx][0]:statsList[idx][2]])
img = cv2.equalizeHist(img)
numIm = image_process.hogFeature(img, (100,100),9,(6,6),(2,2),'L2')
feat.append(numIm)
predictNum = svm_num.predict(np.float32(feat))[1].ravel()
predictList[idx]= predictNum
# draw bounding box
for index,predict in enumerate(predictList):
if predict > 0:
drawBoundingBox(statsList[index], predictList[index],rgb)
print("predicted signs: %s" % predictList)
# validation
num = dic_shapes[category]
valid(predictList, num)
def test_check_item_type(self):
"""item型チェック
"""
data = {"items": [{"item": True, "sequence": 123}]}
self.assertNotEqual(validate.valid(data), "OK")
def test_check_items_type(self):
"""items型チェック
"""
data = {"items": {"item": "coffee", "sequence": 12356}}
self.assertNotEqual(validate.valid(data), "OK")
def test_check_sequence_digit(self):
"""sequence桁数チェック
"""
data = {"items": [{"item": "coffee", "sequence": 12356}]}
self.assertNotEqual(validate.valid(data), "OK")