def save_specific(to_save, full=False, gray=False): prep = Preprocessor(crop_style=1, gray=gray, should_normalize=False) for i in to_save: if full: save_full(i, trial[i]) else: ball, car = prep.process_frame(trial[i]) save(i, ball, car, gray)
def generateSummary(PATH, SIZE): p = Preprocessor(PATH, 1).parse() scorer = SentenceScoreCalculator(p) summary = sorted( sorted(range(p.sCount), key=lambda s: scorer.rank(s), reverse=True)[0:SIZE]) return map(lambda s: p.sentences[s], summary)
def init(): prep = Preprocessor() trial = np.load('data/trial_{}.npy'.format(1)) ball_crop = prep.get_ball_batch(trial).astype(np.float32) global ball_mean, ball_mse_mean, ball_mse_std ball_mean = np.mean(ball_crop, axis=0) mses = np.mean(np.square(ball_crop - ball_mean), axis=(1, 2, 3)) ball_mse_mean = mses.mean() ball_mse_std = mses.std()
def main(): prep = Preprocessor(crop_style=1, should_normalize=False) for i, frame in enumerate(trial): print(i) ball, car = prep.get_ball_and_car(frame) show(car) cv2.waitKey(0) keys = key_check() while not (len(keys) == 1 and ('0' in keys or '1' in keys)): time.sleep(0.1) keys = key_check() if '1' in keys: save(i, ball, car) cv2.destroyAllWindows()
def __init__( self, crop_style=0, gray=False, frame_time=1 / 8, read_only=False, normalize=True, buffer_size=3, field='champions', reward_func=lambda x: 1, ): self.field = field self.crop_style = crop_style if crop_style is None: self.prep = None else: self.prep = Preprocessor(crop_style, gray=gray, should_normalize=normalize, field=field) if gray: self.ball_obs_dims = self.prep.ball_dims + (buffer_size, ) self.car_obs_dims = self.prep.car_dims + (buffer_size, ) else: self.ball_obs_dims = self.prep.ball_dims + (3 * buffer_size, ) self.car_obs_dims = self.prep.car_dims + (3 * buffer_size, ) self.bboxes = (get_screen_bbox(), ) self.next_frame = time.time() self.read_only = read_only self.frame_time = frame_time self.get_reward = reward_func self.gray = gray self.frame_buffer = deque(maxlen=buffer_size) # Save last three frames self.frame_times = [] self.obs_times = []
def generateSummary(PATH, NGRAMS, CLUSTERS, TYPE): # Preprocessing and Term Selection p = Preprocessor(PATH, NGRAMS).parse() counter = WordCounter(p.sCount).count(p.processed) # Term weighting and feature selection features = buildFeatures(p, counter, TYPE) # Sentence clustering kmeans = KMeans( n_clusters=CLUSTERS, #n_jobs=4, precompute_distances=True, init="k-means++", random_state=500, tol=1e-10, copy_x=True) kmeans.fit(features) #Sentence selection summary = sentenceSelect(kmeans, p, features) return map(lambda s: p.sentences[s], sorted(summary))
help="Enables the stopwords' filter (using nltk's stopwords list).", action="store_true") parser.add_argument("-t", help="Enables the stopwords' filter (using pos tag).", action="store_true") parser.add_argument("-u", help="Enables the url filter.", action="store_true") parser.add_argument("-n", help="Enables the not adapter.", action="store_true") args = parser.parse_args() ######### Object creation # timer used for timing timer = Timer() # a preprocessor preprocess = Preprocessor() print "" print "" # Classifier selection if (args.classifier == 'svm'): from classifier.svmClassifier import SVMClassifier print "Classifier: SVM Classifier." classifier = SVMClassifier() else: if (args.classifier == 'bayes'): from classifier.bayesianClassifier import BayesianClassifier print "Classifier: Bayesian Classifier." classifier = BayesianClassifier() else: from classifier.shortTextClassifier import ShortTextClassifier
# print(i, mses[index]) # show(trial[index]) # # print(i, mses[i]) # # show(trial[i]) def show_gray(img): cv2.imshow('cropped', img) cv2.waitKey(0) cv2.destroyAllWindows() gray_minframe = gray_mses.argmin() gray_maxframe = gray_mses.argmax() show_gray(gray_crop[gray_minframe].reshape(gray_mean.shape)) show_gray(gray_mean) show_gray(gray_crop[gray_maxframe].reshape(gray_mean.shape)) show_gray(gray_mean) if __name__ == '__main__': prep = Preprocessor(crop_style=1) # 13 trials for trial_num in range(7): print('Displaying trial {}'.format(trial_num)) trial = np.load('data/champions/trial_{}.npy'.format(trial_num)) show(trial[0]) ball, car = prep.get_ball_and_car(trial[0]) show(ball) show(car) # ball_crop(trial)