mirror of
https://github.com/wassname/IndicoIo-python.git
synced 2026-06-27 16:10:34 +08:00
Merge pull request #104 from IndicoDataSolutions/development
Development
This commit is contained in:
Madison May
+2
-1
@@ -31,4 +31,5 @@ v0.7.4 Mon Jun 22 -- Fix for setup.py issues
|
||||
v0.7.5 Wed Jul 1 -- Public access to sentimentHQ api
|
||||
v0.7.6 Tue Jul 7 -- Add Keywords API
|
||||
v0.8.0 Fri Jul 10 -- Add Content Filtering API, Named Entities API, Facial Emotion with Localization
|
||||
v0.8.1 Wed Fri 22 -- Add Sentiment HQ to predict_text API
|
||||
v0.8.1 Wed Jul 22 -- Add Sentiment HQ to predict_text API
|
||||
v0.9.0 Tue Jul 28 -- Deprecate batch function calls in favor of type inference
|
||||
|
||||
@@ -83,11 +83,11 @@ Examples
|
||||
|
||||
Batch API
|
||||
---------
|
||||
Each `indicoio` function has a corresponding batch function for analyzing many examples with a single request. Simply pass in a list of inputs and receive a list of results in return.
|
||||
Each `indicoio` function can process many examples with a single request. Simply pass in a list of inputs and receive a list of results in return.
|
||||
```python
|
||||
>>> from indicoio import batch_sentiment
|
||||
>>> from indicoio import sentiment
|
||||
|
||||
>>> batch_sentiment(['Best day ever', 'Worst day ever'])
|
||||
>>> sentiment(['Best day ever', 'Worst day ever'])
|
||||
[0.9899001220871786, 0.005709885173415242]
|
||||
```
|
||||
|
||||
@@ -101,12 +101,12 @@ Accepted text API names: `text_tags, political, sentiment, language`
|
||||
Accepted image API names: `fer, facial_features, image_features`
|
||||
|
||||
```python
|
||||
>>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
|
||||
>>> from indicoio import predict_text, predict_image, predict_text, predict_image
|
||||
|
||||
>>> predict_text('Best day ever', apis=["sentiment", "language"])
|
||||
{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
|
||||
|
||||
>>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
|
||||
>>> predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
|
||||
{'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
|
||||
|
||||
>>> import numpy as np
|
||||
@@ -116,6 +116,6 @@ Accepted image API names: `fer, facial_features, image_features`
|
||||
>>> predict_image(test_face, apis=["fer", "facial_features"])
|
||||
{'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
|
||||
|
||||
>>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
|
||||
>>> predict_image([test_face, test_face], apis=["fer", "facial_features"])
|
||||
{'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}
|
||||
```
|
||||
|
||||
+8
-8
@@ -91,15 +91,15 @@ Examples
|
||||
Batch API
|
||||
---------
|
||||
|
||||
Each ``indicoio`` function has a corresponding batch function for
|
||||
analyzing many examples with a single request. Simply pass in a list of
|
||||
inputs and receive a list of results in return.
|
||||
Each ``indicoio`` function can process many examples with a single
|
||||
request. Simply pass in a list of inputs and receive a list of results
|
||||
in return.
|
||||
|
||||
.. code:: python
|
||||
|
||||
>>> from indicoio import batch_sentiment
|
||||
>>> from indicoio import sentiment
|
||||
|
||||
>>> batch_sentiment(['Best day ever', 'Worst day ever'])
|
||||
>>> sentiment(['Best day ever', 'Worst day ever'])
|
||||
[0.9899001220871786, 0.005709885173415242]
|
||||
|
||||
Calling multiple APIs with a single function
|
||||
@@ -119,12 +119,12 @@ Accepted image API names: ``fer, facial_features, image_features``
|
||||
|
||||
.. code:: python
|
||||
|
||||
>>> from indicoio import predict_text, predict_image, batch_predict_text, batch_predict_image
|
||||
>>> from indicoio import predict_text, predict_image, predict_text, predict_image
|
||||
|
||||
>>> predict_text('Best day ever', apis=["sentiment", "language"])
|
||||
{'sentiment': 0.9899001220871786, 'language': {u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, ...}}
|
||||
|
||||
>>> batch_predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
|
||||
>>> predict_text(['Best day ever', 'Worst day ever'], apis=["sentiment", "language"])
|
||||
{'sentiment': [0.9899001220871786, 0.005709885173415242], 'language': [{u'Swedish': 0.0022464881013042294, u'Vietnamese': 9.887170914498351e-05, u'Romanian': 0.00010661175919993216, ...}, {u'Swedish': 0.4924352805804646, u'Vietnamese': 0.028574824174911372, u'Romanian': 0.004185623723173551, u'Dutch': 0.000717033819689362, u'Korean': 0.0030093489153785826, ...}]}
|
||||
|
||||
>>> import numpy as np
|
||||
@@ -134,6 +134,6 @@ Accepted image API names: ``fer, facial_features, image_features``
|
||||
>>> predict_image(test_face, apis=["fer", "facial_features"])
|
||||
{'facial_features': [0.0, -0.026176479280200796, 0.20707644777495776, ...], 'fer': {u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}}
|
||||
|
||||
>>> batch_predict_image([test_face, test_face], apis=["fer", "facial_features"])
|
||||
>>> predict_image([test_face, test_face], apis=["fer", "facial_features"])
|
||||
{'facial_features': [[0.0, -0.026176479280200796, 0.20707644777495776, ...], [0.0, -0.026176479280200796, 0.20707644777495776, ...]], 'fer': [{u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}, { u'Angry': 0.08877494466353497, u'Sad': 0.3933999409104264, u'Neutral': 0.1910612654566151, u'Surprise': 0.0346146405941845, u'Fear': 0.17682159820518667, u'Happy': 0.11532761017005204}]}
|
||||
|
||||
|
||||
+22
-4
@@ -1,6 +1,7 @@
|
||||
from functools import partial
|
||||
from functools import wraps, partial
|
||||
import warnings
|
||||
|
||||
Version, version, __version__, VERSION = ('0.8.1',) * 4
|
||||
Version, version, __version__, VERSION = ('0.9.0',) * 4
|
||||
|
||||
JSON_HEADERS = {
|
||||
'Content-type': 'application/json',
|
||||
@@ -23,9 +24,26 @@ from indicoio.utils.multi import predict_image, predict_text
|
||||
|
||||
from indicoio.config import API_NAMES
|
||||
|
||||
def deprecation_decorator(f, api):
|
||||
@wraps(f)
|
||||
def wrapper(*args, **kwargs):
|
||||
warnings.warn(
|
||||
"'batch_" + api + "' will be deprecated in the next major update. Please call '" + api + "' instead with the same arguments.",
|
||||
DeprecationWarning
|
||||
)
|
||||
return f(*args, **kwargs)
|
||||
return wrapper
|
||||
|
||||
def detect_batch_decorator(f):
|
||||
@wraps(f)
|
||||
def wrapper(*args, **kwargs):
|
||||
if isinstance(args[0], list):
|
||||
kwargs['batch'] = True
|
||||
return f(*args, **kwargs)
|
||||
return wrapper
|
||||
|
||||
apis = dict((api, globals().get(api)) for api in API_NAMES)
|
||||
|
||||
for api in apis:
|
||||
globals()[api] = partial(apis[api])
|
||||
globals()['batch_' + api] = partial(apis[api], batch=True)
|
||||
globals()[api] = partial(detect_batch_decorator(apis[api]))
|
||||
globals()['batch_' + api] = partial(deprecation_decorator(apis[api], api), batch=True)
|
||||
|
||||
@@ -24,6 +24,6 @@ def content_filtering(image, cloud=None, batch=False, api_key=None, **kwargs):
|
||||
:type image: list of lists
|
||||
:rtype: float of nsfwness
|
||||
"""
|
||||
image = image_preprocess(image, batch=batch, size=None)
|
||||
image = image_preprocess(image, batch=batch, size=None, min_axis=128)
|
||||
url_params = {"batch": batch, "api_key": api_key}
|
||||
return api_handler(image, cloud=cloud, api="contentfiltering", url_params=url_params, **kwargs)
|
||||
|
||||
@@ -8,12 +8,15 @@ from indicoio.utils.errors import IndicoError, DataStructureException
|
||||
from indicoio import JSON_HEADERS
|
||||
from indicoio import config
|
||||
|
||||
def api_handler(arg, cloud, api, url_params = {"batch":False, "api_key":None}, **kwargs):
|
||||
def api_handler(arg, cloud, api, url_params=None, **kwargs):
|
||||
if url_params is None:
|
||||
url_params = {"api_key":None, batch:False }
|
||||
|
||||
data = {'data': arg}
|
||||
data.update(**kwargs)
|
||||
json_data = json.dumps(data)
|
||||
if not cloud:
|
||||
cloud=config.cloud
|
||||
cloud = config.cloud
|
||||
|
||||
if cloud:
|
||||
host = "%s.indico.domains" % cloud
|
||||
|
||||
+22
-46
@@ -10,7 +10,7 @@ from indicoio.utils.errors import IndicoError, DataStructureException
|
||||
|
||||
B64_PATTERN = re.compile("^([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)")
|
||||
|
||||
def image_preprocess(image, size=(48,48), batch=False):
|
||||
def image_preprocess(image, size=(48,48), min_axis=None, batch=False):
|
||||
"""
|
||||
Takes an image and prepares it for sending to the api including
|
||||
resizing and image data/structure standardizing.
|
||||
@@ -26,14 +26,8 @@ def image_preprocess(image, size=(48,48), batch=False):
|
||||
elif B64_PATTERN.match(b64_str) is not None:
|
||||
return b64_str
|
||||
else:
|
||||
raise IndicoError("Snose tring provided must be a valid filepath or base64 encoded string")
|
||||
raise IndicoError("String provided must be a valid filepath or base64 encoded string")
|
||||
|
||||
elif isinstance(image, list): # image passed in is a list and not np.array
|
||||
warnings.warn(
|
||||
"Input as lists of pixels will be deprecated in the next major update",
|
||||
DeprecationWarning
|
||||
)
|
||||
out_image = process_list_image(image)
|
||||
elif isinstance(image, Image.Image):
|
||||
out_image = image
|
||||
elif type(image).__name__ == "ndarray": # image is from numpy/scipy
|
||||
@@ -47,9 +41,7 @@ def image_preprocess(image, size=(48,48), batch=False):
|
||||
else:
|
||||
raise IndicoError("Image must be a filepath, base64 encoded string, or a numpy array")
|
||||
|
||||
# image resizing
|
||||
if size:
|
||||
out_image = out_image.resize(size)
|
||||
out_image = resize_image(out_image, size, min_axis)
|
||||
|
||||
# convert to base64
|
||||
temp_output = StringIO.StringIO()
|
||||
@@ -60,6 +52,25 @@ def image_preprocess(image, size=(48,48), batch=False):
|
||||
return base64.b64encode(output_s)
|
||||
|
||||
|
||||
def resize_image(image, size, min_axis):
|
||||
if size:
|
||||
image = image.resize(size)
|
||||
if min_axis:
|
||||
min_idx, other_idx = (0,1) if image.size[0] < image.size[1] else (1,0)
|
||||
aspect = image.size[other_idx]/float(image.size[min_idx])
|
||||
if aspect > 10:
|
||||
warnings.warn(
|
||||
"An aspect ratio greater than 10:1 is not recommended",
|
||||
Warning
|
||||
)
|
||||
size_arr = [0,0]
|
||||
size_arr[min_idx] = min_axis
|
||||
size_arr[other_idx] = int(min_axis * aspect)
|
||||
image = image.resize(tuple(size_arr))
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def get_list_dimensions(_list):
|
||||
"""
|
||||
Takes a nested list and returns the size of each dimension followed
|
||||
@@ -80,38 +91,3 @@ def get_element_type(_list, dimens):
|
||||
elem = elem[0]
|
||||
return type(elem)
|
||||
|
||||
|
||||
def process_list_image(_list):
|
||||
"""
|
||||
Processes list to be [[(int, int, int), ...]]
|
||||
"""
|
||||
# Check if list is empty
|
||||
if not _list:
|
||||
return _list
|
||||
|
||||
dimens = get_list_dimensions(_list)
|
||||
data_type = get_element_type(_list, dimens)
|
||||
|
||||
seq_obj = []
|
||||
|
||||
out_image = Image.new("RGB", (dimens[0], dimens[1]))
|
||||
for i in xrange(dimens[0]):
|
||||
for j in xrange(dimens[1]):
|
||||
elem = _list[i][j]
|
||||
if len(dimens) >= 3:
|
||||
#RGB(A)
|
||||
if data_type == float:
|
||||
seq_obj.append((int(elem[0] * 255), int(elem[1] * 255), int(elem[2] * 255)))
|
||||
else:
|
||||
seq_obj.append(tuple(elem[0:3]))
|
||||
elif data_type == float:
|
||||
#Grayscale 0 - 1.0f
|
||||
seq_obj.append((int(elem * 255), ) * 3)
|
||||
else:
|
||||
#Grayscale 0 - 255
|
||||
seq_obj.append((elem, ) * 3)
|
||||
|
||||
#Needs to be 0 - 255 in flattened list of (R, G, B)
|
||||
out_image.putdata(data = seq_obj)
|
||||
|
||||
return out_image
|
||||
|
||||
@@ -9,7 +9,7 @@ except ImportError:
|
||||
|
||||
setup(
|
||||
name="IndicoIo",
|
||||
version="0.8.1",
|
||||
version="0.9.0",
|
||||
packages=[
|
||||
"indicoio",
|
||||
"indicoio.text",
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 2.4 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 6.9 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 88 B |
Binary file not shown.
|
After Width: | Height: | Size: 4.1 KiB |
+52
-51
@@ -31,74 +31,74 @@ class BatchAPIRun(unittest.TestCase):
|
||||
|
||||
def test_batch_texttags(self):
|
||||
test_data = ["On Monday, president Barack Obama will be..."]
|
||||
response = batch_text_tags(test_data, api_key=self.api_key)
|
||||
response = text_tags(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
|
||||
def test_batch_keywords(self):
|
||||
test_data = ["A working api is key to the success of our young company"]
|
||||
words = [set(text.lower().split()) for text in test_data]
|
||||
response = batch_keywords(test_data, api_key=self.api_key)
|
||||
response = keywords(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(set(response[0].keys()).issubset(words[0]))
|
||||
|
||||
def test_batch_posneg(self):
|
||||
test_data = ['Worst song ever', 'Best song ever']
|
||||
response = batch_sentiment(test_data, api_key=self.api_key)
|
||||
response = sentiment(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(response[0] < 0.5)
|
||||
|
||||
def test_batch_sentiment_hq(self):
|
||||
test_data = ['Worst song ever', 'Best song ever']
|
||||
response = batch_sentiment_hq(test_data, api_key=self.api_key)
|
||||
response = sentiment_hq(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(response[0] < 0.5)
|
||||
|
||||
def test_batch_political(self):
|
||||
test_data = ["Guns don't kill people, people kill people."]
|
||||
response = batch_political(test_data, api_key=self.api_key)
|
||||
response = political(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
|
||||
def test_batch_fer(self):
|
||||
test_data = [generate_array((48,48))]
|
||||
response = batch_fer(test_data, api_key=self.api_key)
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
|
||||
response = fer(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], dict))
|
||||
|
||||
def test_batch_content_filtering(self):
|
||||
test_data = [generate_array((48,48))]
|
||||
response = batch_content_filtering(test_data, api_key=self.api_key)
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
|
||||
response = content_filtering(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], float))
|
||||
|
||||
def test_batch_fer_bad_b64(self):
|
||||
test_data = ["$bad#FI jeaf9(#0"]
|
||||
self.assertRaises(IndicoError, batch_fer, test_data, api_key=self.api_key)
|
||||
self.assertRaises(IndicoError, fer, test_data, api_key=self.api_key)
|
||||
|
||||
def test_batch_fer_good_b64(self):
|
||||
test_data = ["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"]
|
||||
response = batch_fer(test_data, api_key=self.api_key)
|
||||
response = fer(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], dict))
|
||||
|
||||
def test_batch_fer_filepath(self):
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/fear.png"))]
|
||||
response = batch_fer(test_data, api_key=self.api_key)
|
||||
response = fer(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], dict))
|
||||
|
||||
def test_batch_fer_pil_image(self):
|
||||
test_data = [Image.open(os.path.normpath(os.path.join(DIR, "data/fear.png")))]
|
||||
response = batch_fer(test_data, api_key=self.api_key)
|
||||
response = fer(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], dict))
|
||||
|
||||
def test_batch_fer_nonexistant_filepath(self):
|
||||
test_data = ["data/unhappy.png"]
|
||||
self.assertRaises(IndicoError, batch_fer, test_data, api_key=self.api_key)
|
||||
self.assertRaises(IndicoError, fer, test_data, api_key=self.api_key)
|
||||
|
||||
def test_batch_facial_features(self):
|
||||
test_data = [generate_array((48,48))]
|
||||
response = batch_facial_features(test_data, api_key=self.api_key)
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
|
||||
response = facial_features(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], list))
|
||||
self.assertEqual(len(response[0]), 48)
|
||||
@@ -117,22 +117,22 @@ class BatchAPIRun(unittest.TestCase):
|
||||
# have decided how we are dealing with them
|
||||
|
||||
def test_batch_image_features_greyscale(self):
|
||||
test_data = [generate_array((48,48))]
|
||||
response = batch_image_features(test_data, api_key=self.api_key)
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
|
||||
response = image_features(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], list))
|
||||
self.assertEqual(len(response[0]), 2048)
|
||||
|
||||
def test_batch_image_features_rgb(self):
|
||||
test_data = [generate_array((48,48))]
|
||||
response = batch_image_features(test_data, api_key=self.api_key)
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48rgb.png"))]
|
||||
response = image_features(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(isinstance(response[0], list))
|
||||
self.assertEqual(len(response[0]), 2048)
|
||||
|
||||
def test_batch_language(self):
|
||||
test_data = ['clearly an english sentence']
|
||||
response = batch_language(test_data, api_key=self.api_key)
|
||||
response = language(test_data, api_key=self.api_key)
|
||||
self.assertTrue(isinstance(response, list))
|
||||
self.assertTrue(response[0]['English'] > 0.25)
|
||||
|
||||
@@ -140,14 +140,15 @@ class BatchAPIRun(unittest.TestCase):
|
||||
batch = ["London Underground's boss Mike Brown warned that the strike ..."]
|
||||
expected_entities = ("London Underground", "Mike Brown")
|
||||
expected_keys = set(["categories", "confidence"])
|
||||
entities = batch_named_entities(batch)[0]
|
||||
entities = named_entities(batch)[0]
|
||||
for entity in expected_entities:
|
||||
assert entity in expected_entities
|
||||
assert not (set(entities[entity]) - expected_keys)
|
||||
|
||||
def test_batch_multi_api_image(self):
|
||||
test_data = [generate_array((48,48)), generate_int_array((48,48))]
|
||||
response = batch_predict_image(test_data, apis=config.IMAGE_APIS, api_key=self.api_key)
|
||||
test_data = [os.path.normpath(os.path.join(DIR, "data/48by48.png")),
|
||||
os.path.normpath(os.path.join(DIR, "data/48by48.png"))]
|
||||
response = predict_image(test_data, apis=config.IMAGE_APIS, api_key=self.api_key)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertTrue(set(response.keys()) == set(config.IMAGE_APIS))
|
||||
@@ -155,21 +156,21 @@ class BatchAPIRun(unittest.TestCase):
|
||||
|
||||
def test_batch_multi_api_text(self):
|
||||
test_data = ['clearly an english sentence']
|
||||
response = batch_predict_text(test_data, apis=config.TEXT_APIS, api_key=self.api_key)
|
||||
response = predict_text(test_data, apis=config.TEXT_APIS, api_key=self.api_key)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertTrue(set(response.keys()) == set(config.TEXT_APIS))
|
||||
|
||||
def test_default_multi_api_text(self):
|
||||
test_data = ['clearly an english sentence']
|
||||
response = batch_predict_text(test_data, api_key=self.api_key)
|
||||
response = predict_text(test_data, api_key=self.api_key)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertTrue(set(response.keys()) == set(config.TEXT_APIS))
|
||||
|
||||
def test_multi_api_bad_api(self):
|
||||
self.assertRaises(IndicoError,
|
||||
batch_predict_text,
|
||||
predict_text,
|
||||
"this shouldn't work",
|
||||
apis=["sentiment", "somethingbad"])
|
||||
|
||||
@@ -180,14 +181,14 @@ class BatchAPIRun(unittest.TestCase):
|
||||
apis=["fer", "sentiment", "facial_features"])
|
||||
def test_batch_multi_bad_mixed_api(self):
|
||||
self.assertRaises(IndicoError,
|
||||
batch_predict_text,
|
||||
predict_text,
|
||||
["this shouldn't work"],
|
||||
apis=["fer", "sentiment", "facial_features"])
|
||||
|
||||
def test_batch_set_cloud(self):
|
||||
test_data = ['clearly an english sentence']
|
||||
self.assertRaises(ConnectionError,
|
||||
batch_language,
|
||||
language,
|
||||
test_data,
|
||||
api_key=self.api_key,
|
||||
cloud='invalid/cloud')
|
||||
@@ -290,7 +291,7 @@ class FullAPIRun(unittest.TestCase):
|
||||
|
||||
def test_good_fer(self):
|
||||
fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
|
||||
test_face = generate_array((48,48))
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
|
||||
response = fer(test_face)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
@@ -298,14 +299,14 @@ class FullAPIRun(unittest.TestCase):
|
||||
|
||||
def test_good_int_array_fer(self):
|
||||
fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
|
||||
test_face = generate_int_array((48,48))
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
|
||||
response = fer(test_face)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertEqual(fer_set, set(response.keys()))
|
||||
|
||||
def test_happy_fer(self):
|
||||
test_face = self.load_image("data/happy.png", as_grey=True)
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
|
||||
response = fer(test_face)
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertTrue(response['Happy'] > 0.5)
|
||||
@@ -317,26 +318,31 @@ class FullAPIRun(unittest.TestCase):
|
||||
self.assertTrue(response['Happy'] > 0.5)
|
||||
|
||||
def test_fear_fer(self):
|
||||
test_face = self.load_image("data/fear.png", as_grey=True)
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/fear.png"))
|
||||
response = fer(test_face)
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertTrue(response['Fear'] > 0.25)
|
||||
|
||||
def test_bad_fer(self):
|
||||
fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
|
||||
test_face = generate_array((56, 56))
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/64by64.png"))
|
||||
response = fer(test_face)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
self.assertEqual(fer_set, set(response.keys()))
|
||||
|
||||
def test_safe_content_filtering(self):
|
||||
test_face = self.load_image("data/happy.png", as_grey=True)
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
|
||||
response = content_filtering(test_face)
|
||||
self.assertTrue(response < 0.5)
|
||||
|
||||
def test_resize_content_filtering(self):
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
|
||||
response = content_filtering(test_face)
|
||||
self.assertTrue(isinstance(response, float))
|
||||
|
||||
def test_good_facial_features(self):
|
||||
test_face = generate_array((48,48))
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
|
||||
response = facial_features(test_face)
|
||||
|
||||
self.assertTrue(isinstance(response, list))
|
||||
@@ -344,7 +350,7 @@ class FullAPIRun(unittest.TestCase):
|
||||
self.check_range(response)
|
||||
|
||||
def test_rgba_int_array_facial_features(self):
|
||||
test_face = generate_rgba_int_array((48, 48))
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/48by48rgba.png"))
|
||||
response = facial_features(test_face)
|
||||
|
||||
self.assertTrue(isinstance(response, list))
|
||||
@@ -353,7 +359,7 @@ class FullAPIRun(unittest.TestCase):
|
||||
|
||||
def test_good_int_array_facial_features(self):
|
||||
fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy'])
|
||||
test_face = generate_int_array((48,48))
|
||||
test_face = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
|
||||
response = facial_features(test_face)
|
||||
|
||||
self.assertTrue(isinstance(response, list))
|
||||
@@ -371,7 +377,7 @@ class FullAPIRun(unittest.TestCase):
|
||||
# self.check_range(response)
|
||||
|
||||
def test_good_image_features_greyscale(self):
|
||||
test_image = generate_array((48,48))
|
||||
test_image = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
|
||||
response = image_features(test_image)
|
||||
|
||||
self.assertTrue(isinstance(response, list))
|
||||
@@ -379,7 +385,7 @@ class FullAPIRun(unittest.TestCase):
|
||||
self.check_range(response)
|
||||
|
||||
def test_good_image_features_rgb(self):
|
||||
test_image = [[(random.random(),) * 3 for _ in xrange(48)] for _ in xrange(48)]
|
||||
test_image = os.path.normpath(os.path.join(DIR, "data/48by48rgb.png"))
|
||||
response = image_features(test_image)
|
||||
|
||||
self.assertTrue(isinstance(response, list))
|
||||
@@ -387,7 +393,7 @@ class FullAPIRun(unittest.TestCase):
|
||||
self.check_range(response)
|
||||
|
||||
def test_multi_api_image(self):
|
||||
test_data = generate_array((48,48))
|
||||
test_data = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
|
||||
response = predict_image(test_data, apis=config.IMAGE_APIS, api_key=self.api_key)
|
||||
|
||||
self.assertTrue(isinstance(response, dict))
|
||||
@@ -550,6 +556,11 @@ class NumpyImagesRun(FullAPIRun):
|
||||
test_image = np.random.randint(255, 300, size=(48,48, 5))
|
||||
self.assertRaises(IndicoError, image_features, test_image)
|
||||
|
||||
def test_resize_content_filtering_numpy_arrays(self):
|
||||
test_image = np.random.randint(0, 255, size=(480,248, 3))
|
||||
response = content_filtering(test_image)
|
||||
self.assertTrue(isinstance(response, float))
|
||||
|
||||
def flatten(container):
|
||||
for i in container:
|
||||
if isinstance(i, list) or isinstance(i, tuple):
|
||||
@@ -558,15 +569,5 @@ def flatten(container):
|
||||
else:
|
||||
yield i
|
||||
|
||||
def generate_array(size):
|
||||
return [[random.random() for _ in xrange(size[0])] for _ in xrange(size[1])]
|
||||
|
||||
def generate_int_array(size):
|
||||
return [[random.randint(0, 255) for _ in xrange(size[0])] for _ in xrange(size[1])]
|
||||
|
||||
def generate_rgba_int_array(size):
|
||||
return [[[random.randint(0, 255) for _ in xrange(3)] for _ in xrange(size[0])] for _ in xrange(size[1])]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
@@ -0,0 +1,16 @@
|
||||
from indicoio.utils.image import image_preprocess
|
||||
from PIL import Image
|
||||
import os, unittest, base64, StringIO
|
||||
|
||||
DIR = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
class ResizeTests(unittest.TestCase):
|
||||
"""
|
||||
test image resizing
|
||||
"""
|
||||
def test_min_axis_resize(self):
|
||||
test_image = os.path.normpath(os.path.join(DIR, "data/fear.png"))
|
||||
resized_image = image_preprocess(test_image, min_axis=360)
|
||||
image_string = StringIO.StringIO(base64.b64decode(resized_image))
|
||||
image = Image.open(image_string)
|
||||
self.assertEqual(image.size, (360.0, 360.0))
|
||||
Reference in New Issue
Block a user