def test_compute_feature(self):
test_image_url = "https://images.homedepot-static.com/productImages/02e7d98c-5f38-4472-9162-b2ca874cfbef/svn/grey-innovative-textile-solutions-slipcovers-9050chaigrey-64_400.jpg"
test_img = ImageClassifierUtils.download_image(test_image_url)
opencv_feature_list = ["average_pixel_width", "touch_the_border", "dark_pctg",
"rotation","dominant_color","number_of_object"]
OpenCVFeatureUtils(opencv_feature_list, test_img).compute_feature()
assert Exception
def generate_classifier_module(self):
classifier_modules_dict = {}
if (self.mode == 'dataflow'):
fs = gcsfs.GCSFileSystem()
with fs.open(self.image_classifier_info_file_path) as image_classifier_catalog:
image_classifier_data_frame = pd.read_json(image_classifier_catalog, lines=True)
image_classifier_dict = image_classifier_data_frame.to_dict('records')[0]
else:
image_classifier_data_frame = pd.read_json(self.image_classifier_info_file_path, lines=True)
image_classifier_dict = image_classifier_data_frame.to_dict('records')[0]
for classifier_module, classifier_module_dtls in image_classifier_dict.items():
classifier_modules_dict[classifier_module] = ImageClassifierUtils.model_file_loader(
classifier_module_dtls)
return classifier_modules_dict;
def process(self, element, image_classifier_tree_file_path,
image_classifier_info_file_path, mode):
if self._model_state is None:
if (getattr(self._thread_local, "model_state", None) is None
or self._thread_local.model_state.
image_classifier_tree_file_path !=
image_classifier_tree_file_path or self._thread_local.
model_state.image_classifier_info_file_path !=
image_classifier_info_file_path
or self._thread_local.model_state.mode != mode):
start = datetime.datetime.now()
self._thread_local.model_state = self._ModelState(
image_classifier_tree_file_path,
image_classifier_info_file_path, mode)
print('time taken to load model %d',
(datetime.datetime.now() - start).total_seconds())
self._model_state = self._thread_local.model_state
else:
assert self._model_state.image_classifier_tree_file_path == image_classifier_tree_file_path
assert self._model_state.image_classifier_info_file_path == image_classifier_info_file_path
thread_classifier_tree_dict = self._model_state.classifier_tree_dict
(oms_id, category_image_dtls_list) = element
label = ""
processed_image_dict = {}
image_classification_list = []
for productCategoryImageDtlsVO in category_image_dtls_list:
category_id = productCategoryImageDtlsVO.category_id
image_url_dtls_list = productCategoryImageDtlsVO.image_dtls_list
#print('category_id',category_id,'image_url_dtls_list',image_url_dtls_list)
#image_classification_list = []
for image_dtls_dict in image_url_dtls_list:
try:
image_url = image_dtls_dict['image_url']
image_id = image_dtls_dict['image_id']
if image_id in processed_image_dict:
json_data = processed_image_dict[image_id]
oms_id = json_data['oms_id']
label = json_data['label']
image_available = json_data['image_available']
image_url = json_data['image_url']
predictions = json_data['predictions']
#print("*****prediction, case 1:", predictions)
category_modified_json_data = GenericUtils.generate_image_classification_json(
oms_id, label, category_id, image_available,
image_url, predictions)
#print("******json, case 1:",category_modified_json_data)
image_classification_list.append(
str(json.dumps(category_modified_json_data)))
else:
image_np_array = ImageClassifierUtils.download_image(
image_url)
if image_np_array.size > 0:
if category_id in thread_classifier_tree_dict:
classifier_tree = thread_classifier_tree_dict[
category_id]
else:
classifier_tree = thread_classifier_tree_dict[
'unfound_category']
predictions = ImageClassifierUtils.climb_classifier_tree(
image_np_array, classifier_tree,
self._model_state.classifier_modules_dict, [])
#print("*****prediction, case 2:",predictions)
image_available = "Y"
json_data = GenericUtils.generate_image_classification_json(
oms_id, label, category_id, image_available,
image_url, predictions)
processed_image_dict[image_id] = json_data
#print("******json, case 2:",json_data)
image_classification_list.append(
str(json.dumps(json_data)))
except Exception:
logging.error('Error in running ProductClassifierMain',
exc_info=True)
image_available = "N"
predictions = ""
json_data = GenericUtils.generate_image_classification_json(
oms_id, label, category_id, image_available, image_url,
predictions)
processed_image_dict[image_id] = json_data
image_classification_list.append(str(
json.dumps(json_data)))
if image_classification_list:
for image_classification in image_classification_list:
yield image_classification