import unittest import os import numpy as np import skimage.io from indicoio import political, sentiment, fer, facial_features, language, image_features, text_tags DIR = os.path.dirname(os.path.realpath(__file__)) class FullAPIRun(unittest.TestCase): def load_image(self, relpath, as_grey=False): image_path = os.path.normpath(os.path.join(DIR, relpath)) image = skimage.io.imread(image_path, as_grey=True).tolist() return image def check_range(self, list, minimum=0.9, maximum=0.1, span=0.5): vector = np.asarray(list) self.assertTrue(vector.max() > maximum) self.assertTrue(vector.min() < minimum) self.assertTrue(np.ptp(vector) > span) def test_text_tags(self): text = "On Monday, president Barack Obama will be..." results = text_tags(text) max_keys = sorted(results.keys(), key=lambda x:results.get(x), reverse=True) assert 'political_discussion' in max_keys[:5] def test_political(self): political_set = set(['Libertarian', 'Liberal', 'Conservative', 'Green']) test_string = "Guns don't kill people, people kill people." response = political(test_string) self.assertTrue(isinstance(response, dict)) self.assertEqual(political_set, set(response.keys())) test_string = "Save the whales" response = political(test_string) self.assertTrue(isinstance(response, dict)) assert response['Green'] > 0.5 def test_posneg(self): test_string = "Worst song ever." response = sentiment(test_string) self.assertTrue(isinstance(response, float)) self.assertTrue(response < 0.5) test_string = "Best song ever." response = sentiment(test_string) self.assertTrue(isinstance(response, float)) self.assertTrue(response > 0.5) def test_good_fer(self): fer_set = set(['Angry', 'Sad', 'Neutral', 'Surprise', 'Fear', 'Happy']) test_face = np.random.rand(48,48).tolist() 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) response = fer(test_face) self.assertTrue(isinstance(response, dict)) self.assertTrue(response['Happy'] > 0.5) def test_fear_fer(self): test_face = self.load_image("../data/fear.png", as_grey=True) 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 = np.random.rand(56,56).tolist() response = fer(test_face) self.assertTrue(isinstance(response, dict)) self.assertEqual(fer_set, set(response.keys())) def test_good_facial_features(self): test_face = np.random.rand(48,48).tolist() response = facial_features(test_face) self.assertTrue(isinstance(response, list)) self.assertEqual(len(response), 48) self.check_range(response) def test_good_image_features_greyscale(self): test_image = np.random.rand(64, 64).tolist() response = image_features(test_image) self.assertTrue(isinstance(response, list)) self.assertEqual(len(response), 2048) self.check_range(response) def test_good_image_features_rgb(self): test_image = np.random.rand(64, 64, 3).tolist() response = image_features(test_image) self.assertTrue(isinstance(response, list)) self.assertEqual(len(response), 2048) self.check_range(response) def test_language(self): language_set = set([ 'English', 'Spanish', 'Tagalog', 'Esperanto', 'French', 'Chinese', 'French', 'Bulgarian', 'Latin', 'Slovak', 'Hebrew', 'Russian', 'German', 'Japanese', 'Korean', 'Portuguese', 'Italian', 'Polish', 'Turkish', 'Dutch', 'Arabic', 'Persian (Farsi)', 'Czech', 'Swedish', 'Indonesian', 'Vietnamese', 'Romanian', 'Greek', 'Danish', 'Hungarian', 'Thai', 'Finnish', 'Norwegian', 'Lithuanian' ]) language_dict = language('clearly an english sentence') self.assertEqual(language_set, set(language_dict.keys())) assert language_dict['English'] > 0.25 if __name__ == "__main__": unittest.main()