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How to use get_region method in localstack

Best Python code snippet using localstack_python

pathology.py

Source:pathology.py

1import tensorflow as tf2def get_region(image_tensor, h, w, position):3    if position == "left_upper":4        image_tensor = image_tensor[:, :h, :w, :]5    elif position == "right_upper":6        image_tensor = image_tensor[:, :h, w:, :]7    elif position == "left_lower":8        image_tensor = image_tensor[:, h:, :w, :]9    elif position == "right_lower":10        image_tensor = image_tensor[:, h:, w:, :]11    return image_tensor12def recon_overlapping_patches(image_tensor):13    # _, H, W, _ = image_tensor_concat.shape14    _, H, W, _ = image_tensor[0].shape15    h, w = H // 2, W // 216    # image_tensor = tf.split(image_tensor_concat, 10, -1)17    image_tensor_list = []18    row_0_col_0 = get_region(image_tensor[0], h, w, "left_upper") * 119    row_0_col_1 = get_region(image_tensor[0], h, w, "right_upper") * \20        0.5 + get_region(image_tensor[1], h, w, "left_upper") * 0.521    row_0_col_2 = get_region(image_tensor[1], h, w, "right_upper") * \22        0.5 + get_region(image_tensor[2], h, w, "left_upper") * 0.523    row_0_col_3 = get_region(image_tensor[2], h, w, "right_upper") * 124    row_1_col_0 = get_region(image_tensor[0], h, w, "left_lower") * \25        0.5 + get_region(image_tensor[3], h, w, "left_upper") * 0.526    row_1_col_1 = get_region(image_tensor[0], h, w, "right_lower") * 0.25 + get_region(image_tensor[1], h, w, "left_lower") * 0.25 + \27        get_region(image_tensor[3], h, w, "right_upper") * 0.25 + \28        get_region(image_tensor[4], h, w, "left_upper") * 0.2529    row_1_col_2 = get_region(image_tensor[1], h, w, "right_lower") * 0.25 + get_region(image_tensor[2], h, w, "left_lower") * 0.25 + \30        get_region(image_tensor[4], h, w, "right_upper") * 0.25 + \31        get_region(image_tensor[5], h, w, "left_upper") * 0.2532    row_1_col_3 = get_region(image_tensor[2], h, w, "right_lower") * \33        0.5 + get_region(image_tensor[5], h, w, "right_upper") * 0.534    row_2_col_0 = get_region(image_tensor[3], h, w, "left_lower") * \35        0.5 + get_region(image_tensor[6], h, w, "left_upper") * 0.536    row_2_col_1 = get_region(image_tensor[3], h, w, "right_lower") * 0.25 + get_region(image_tensor[4], h, w, "left_lower") * 0.25 + \37        get_region(image_tensor[6], h, w, "right_upper") * 0.25 + \38        get_region(image_tensor[7], h, w, "left_upper") * 0.2539    row_2_col_2 = get_region(image_tensor[4], h, w, "right_lower") * 0.25 + get_region(image_tensor[5], h, w, "left_lower") * 0.25 + \40        get_region(image_tensor[7], h, w, "right_upper") * 0.25 + \41        get_region(image_tensor[8], h, w, "left_upper") * 0.2542    row_2_col_3 = get_region(image_tensor[5], h, w, "right_lower") * \43        0.5 + get_region(image_tensor[8], h, w, "right_upper") * 0.544    row_3_col_0 = get_region(image_tensor[6], h, w, "left_lower") * 145    row_3_col_1 = get_region(image_tensor[6], h, w, "right_lower") * \46        0.5 + get_region(image_tensor[7], h, w, "left_lower") * 0.547    row_3_col_2 = get_region(image_tensor[7], h, w, "right_lower") * \48        0.5 + get_region(image_tensor[8], h, w, "left_lower") * 0.549    row_3_col_3 = get_region(image_tensor[8], h, w, "right_lower") * 150    image_tensor_list.append(row_0_col_0)51    image_tensor_list.append(row_0_col_1)52    image_tensor_list.append(row_0_col_2)53    image_tensor_list.append(row_0_col_3)54    image_tensor_list.append(row_1_col_0)55    image_tensor_list.append(row_1_col_1)56    image_tensor_list.append(row_1_col_2)57    image_tensor_list.append(row_1_col_3)58    image_tensor_list.append(row_2_col_0)59    image_tensor_list.append(row_2_col_1)60    image_tensor_list.append(row_2_col_2)61    image_tensor_list.append(row_2_col_3)62    image_tensor_list.append(row_3_col_0)63    image_tensor_list.append(row_3_col_1)64    image_tensor_list.append(row_3_col_2)65    image_tensor_list.append(row_3_col_3)66    row_1 = tf.concat(image_tensor_list[:4], axis=2)67    row_2 = tf.concat(image_tensor_list[4:8], axis=2)68    row_3 = tf.concat(image_tensor_list[8:12], axis=2)69    row_4 = tf.concat(image_tensor_list[12:], axis=2)70    restored = tf.concat([row_1, row_2, row_3, row_4], axis=1)71    return restored72# 1 / 4 Scale Restore73def recon_overlapping_patches_quarter_scale(image_tensor):74    # _, H, W, _ = image_tensor_concat.shape75    _, H, W, _ = image_tensor[0].shape76    h, w = H // 2, W // 277    col_in_row = 778    row_list = []79    col_list = []80    col_list.append(get_region(image_tensor[0], h, w, "left_upper") * 1)81    for idx in range(col_in_row - 1):82        col_element = get_region(image_tensor[idx], h, w, "right_upper") * \83            0.5 + get_region(image_tensor[idx + 1], h, w, "left_upper") * 0.584        col_list.append(col_element)85    col_list.append(get_region(86        image_tensor[col_in_row - 1], h, w, "right_upper") * 1)87    row_list.append(col_list)88    for row_idx in range(col_in_row - 1):89        start_num = row_idx * col_in_row90        col_list = []91        col_list.append(get_region(image_tensor[start_num], h, w, "left_lower") *92                        0.5 + get_region(image_tensor[start_num + col_in_row], h, w, "left_upper") * 0.5)93        for col_idx in range(col_in_row - 1):94            col_element = get_region(image_tensor[start_num + col_idx], h, w, "right_lower") * 0.25 + \95                get_region(image_tensor[start_num + col_idx + 1], h, w, "left_lower") * 0.25 + \96                get_region(image_tensor[start_num + col_idx + col_in_row], h, w, "right_upper") * 0.25 + \97                get_region(image_tensor[start_num + col_idx + col_in_row + 1],98                           h, w, "left_upper") * 0.2599            col_list.append(col_element)100        col_list.append(get_region(image_tensor[start_num + col_in_row - 1], h, w, "right_lower") *101                        0.5 + get_region(image_tensor[start_num + col_in_row + col_in_row - 1], h, w, "right_upper") * 0.5)102        row_list.append(col_list)103    col_list = []104    start_num = (col_in_row - 1) * col_in_row105    col_list.append(get_region(106        image_tensor[start_num], h, w, "left_lower") * 1)107    for idx in range(col_in_row - 1):108        col_element = get_region(image_tensor[start_num + idx], h, w, "right_lower") * \109            0.5 + \110            get_region(image_tensor[start_num + idx + 1],111                       h, w, "left_lower") * 0.5112        col_list.append(col_element)113    col_list.append(get_region(114        image_tensor[start_num + col_in_row - 1], h, w, "right_lower") * 1)115    row_list.append(col_list)116    row_list = [tf.concat(row, axis=2) for row in row_list]117    restored = tf.concat(row_list, axis=1)...

font_data.py

Source:font_data.py

...3resource.path.append('res')45highlighted_font_image = resource.image('font.png')6Outlined = {7    "A" : [highlighted_font_image.get_region(0, 0, 25,25), 0, 0],8    "B" : [highlighted_font_image.get_region(25, 0, 25,25), 0, 0],9    "C" : [highlighted_font_image.get_region(50, 0, 25,25), 0, 0],10    "D" : [highlighted_font_image.get_region(75, 0, 25,25), 0, 0],11    "E" : [highlighted_font_image.get_region(99, 0, 26,25), 0, 0],12    "F" : [highlighted_font_image.get_region(125, 0, 25,25), 0, 0],13    "G" : [highlighted_font_image.get_region(150, 0, 25,25), 0, 0],14    "H" : [highlighted_font_image.get_region(175, 0, 25,25), 0, 0],15    "I" : [highlighted_font_image.get_region(206, 0, 12,25), 0, 0],16    "J" : [highlighted_font_image.get_region(225, 0, 25,25), 0, 0],17    "K" : [highlighted_font_image.get_region(250, 0, 25,25), 0, 0],18    "L" : [highlighted_font_image.get_region(275, 0, 22,25), 0, 0],19    "M" : [highlighted_font_image.get_region(300, 0, 25,25), 0, 0],20    "N" : [highlighted_font_image.get_region(325, 0, 25,25), 0, 0],21    "O" : [highlighted_font_image.get_region(349, 0, 27,26), 0, 0],22    "P" : [highlighted_font_image.get_region(375, 0, 25,25), 0, 0],23    "Q" : [highlighted_font_image.get_region(400, 0, 25,25), 0, 0],24    "R" : [highlighted_font_image.get_region(425, 0, 25,25), 0, 0],25    "S" : [highlighted_font_image.get_region(450, 0, 25,25), 0, 0],26    "T" : [highlighted_font_image.get_region(475, 0, 25,25), 0, 0],27    "U" : [highlighted_font_image.get_region(500, 0, 25,25), 0, 0],28    "V" : [highlighted_font_image.get_region(525, 0, 25,25), 0, 0],29    "W" : [highlighted_font_image.get_region(550, 0, 37,25), 0, 0],30    "X" : [highlighted_font_image.get_region(587, 0, 25,25), 0, 0],31    "Y" : [highlighted_font_image.get_region(614, 0, 23,25), 0, 0],32    "Z" : [highlighted_font_image.get_region(636, 0, 25,25), 0, 0],33    " " : [highlighted_font_image.get_region(660, 0, 15,25), 0, 0],34    35    #Lower-case36    "a" : [highlighted_font_image.get_region(2, 36, 17,25), 0, -1],37    "b" : [highlighted_font_image.get_region(27, 35, 19,28), 0, -3],38    "c" : [highlighted_font_image.get_region(53, 36, 17,25), 0, -1],39    "d" : [highlighted_font_image.get_region(77, 36, 19, 33), 0, -2],40    "e" : [highlighted_font_image.get_region(103, 36, 19, 33), 0, -1],41    "f" : [highlighted_font_image.get_region(130, 36, 14, 33), 0, -2],42    "g" : [highlighted_font_image.get_region(152, 32, 19, 39), 0, -4],43    "h" : [highlighted_font_image.get_region(177, 34, 18, 39), 0, -2],44    "i" : [highlighted_font_image.get_region(207, 32, 10, 39), 0, -3],45    "j" : [highlighted_font_image.get_region(231, 30, 9, 39), 0, -4],46    "k" : [highlighted_font_image.get_region(248, 33, 20, 34), 0, -3],47    "l" : [highlighted_font_image.get_region(284, 33, 7, 34), 0, -3],48    "m" : [highlighted_font_image.get_region(298, 33, 28, 24), 0, -2],49    "n" : [highlighted_font_image.get_region(328, 33, 18, 34), 0, -3],50    "o" : [highlighted_font_image.get_region(352, 33, 20, 34), 0, -3],51    "p" : [highlighted_font_image.get_region(376, 30, 19, 39), 0, -6],52    "q" : [highlighted_font_image.get_region(400, 30, 19, 39), 0, -6],53    "r" : [highlighted_font_image.get_region(429, 34, 12, 35), 0, -1],54    "s" : [highlighted_font_image.get_region(453, 34, 16, 35), 0, -1],55    "t" : [highlighted_font_image.get_region(479, 34, 16, 35), 0, -1],56    "u" : [highlighted_font_image.get_region(504, 34, 16, 35), 0, -1],57    "v" : [highlighted_font_image.get_region(527, 34, 18, 35), 0, -1],58    "w" : [highlighted_font_image.get_region(555, 34, 28, 35), 0, -1],59    "x" : [highlighted_font_image.get_region(591, 34, 18, 35), 0, -1],60    "y" : [highlighted_font_image.get_region(617, 30, 19, 39), 0, -7],61    "z" : [highlighted_font_image.get_region(642, 30, 18, 34), 0, -4.5],62    63    #Numbers64    "0" : [highlighted_font_image.get_region(4, 76, 21, 34), 0, -0.5],65    "1" : [highlighted_font_image.get_region(33, 76, 18, 35), 0, -0.5],66    "2" : [highlighted_font_image.get_region(59, 76, 19, 35), 0, -0.5],67    "3" : [highlighted_font_image.get_region(86, 76, 19, 35), 0, -0.5],68    "4" : [highlighted_font_image.get_region(112, 76, 21, 35), 0, -0.5],69    "5" : [highlighted_font_image.get_region(140, 76, 19, 35), 0, -0.5],70    "6" : [highlighted_font_image.get_region(167, 76, 19, 35), 0, -0.5],71    "7" : [highlighted_font_image.get_region(194, 76, 19, 35), 0, -0.5],72    "8" : [highlighted_font_image.get_region(220, 76, 20, 35), 0, -0.5],73    "9" : [highlighted_font_image.get_region(255, 76, 19, 35), 0, -0.5],74    75    #Special76    ":" : [highlighted_font_image.get_region(284, 76, 8, 35), 0, -0.5],77    "," : [highlighted_font_image.get_region(300, 70, 11, 35), -1, -5],78    "'" : [highlighted_font_image.get_region(354, 76, 6, 35), 0, -0.5],79    r'"' : [highlighted_font_image.get_region(370, 76, 12, 35), 0, -0.5],80    "." : [highlighted_font_image.get_region(319, 76, 8, 32), 0, -0.5],81    "?" : [highlighted_font_image.get_region(7, 112, 16, 32), 0, -0.5],82    "!" : [highlighted_font_image.get_region(35, 112, 8, 32), 0, -0.5],
...

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