Print "Error in reading data to db- number of channels must be 1 or 2" Orig_im = np.zeros((MAT_SHAPE,MAT_SHAPE,3)) Im = np.zeros((int(MAT_SHAPE*DOWNSCALE_FACTOR),int(MAT_SHAPE*DOWNSCALE_FACTOR),3)) Index, data, base_filename, db_name, C, aug_data = params Install scipy doing 'pip3 install numpy' and 'pip3 install scipy' or read here: ```") Peak = codes] # gets the original indexĬolors.append(''.join(format(c, '02x') for c in peak)) Sorted_list = sorted(freq_index, key=emgetter(1), reverse=True) Vecs, dist = .vq(ar, codes) # assign codesĬounts, bins = scipy.histogram(vecs, len(codes)) # count occurrences Im = im.resize((290, 290)) # resized to reduce timeĪr = ar.reshape(scipy.product(shape), shape)Ĭodes, dist = .kmeans(ar.astype(float), clusters) Im = Image.open('data/leveler/temp_auto.png').convert('RGBA') With open('data/leveler/temp_auto.png','wb') as f: Phrases = # in case I want moreĪwait ("****".format(random.choice(phrases))) Output_lab_no_temp = 2rgb(output_lab_no_temp) Output_lab_no_temp = np.concatenate((inp_rescale, images_resized), axis=3).astype(np.float64) Inp_rescale = rescale_l_for_display(input_batch) Images_resized = np.append(images_resized, np.expand_dims(temp, axis=0), axis=0)
Images_resized = np.zeros(, dtype=np.float32)įor image in range(gen_target_no_temp.shape): Gen_target_no_temp = np.dot(softmax, kernel) Kernel = np.load('/home/ubuntu/task-taxonomy-331b/lib/data/pts_in_hull.npy') Softmax = softmax / np.expand_dims(sums, -1) Softmax = np.exp(predicted - np.expand_dims(maxs, axis=-1)) Def single_img_colorize( predicted, input_batch, to_store_name ):
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