How to use get_region method in localstack

Best Python code snippet using localstack_python

pathology.py

Source: pathology.py Github

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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)...

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font_data.py

Source: font_data.py Github

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...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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