How to use ls method in fMBT

Best Python code snippet using fMBT_python

non_greedy_mv.py

Source:non_greedy_mv.py Github

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...11from matplotlib.collections import LineCollection12from matplotlib import colors as mcolors13import numpy as np14import math15def draw_mv_ls(axis, mv_ls, mode=0):16 colors = np.array([(1., 0., 0., 1.)])17 segs = np.array([18 np.array([[ptr[0], ptr[1]], [ptr[0] + ptr[2], ptr[1] + ptr[3]]])19 for ptr in mv_ls20 ])21 line_segments = LineCollection(22 segs, linewidths=(1.,), colors=colors, linestyle='solid')23 axis.add_collection(line_segments)24 if mode == 0:25 axis.scatter(mv_ls[:, 0], mv_ls[:, 1], s=2, c='b')26 else:27 axis.scatter(28 mv_ls[:, 0] + mv_ls[:, 2], mv_ls[:, 1] + mv_ls[:, 3], s=2, c='b')29def draw_pred_block_ls(axis, mv_ls, bs, mode=0):30 colors = np.array([(0., 0., 0., 1.)])31 segs = []32 for ptr in mv_ls:33 if mode == 0:34 x = ptr[0]35 y = ptr[1]36 else:37 x = ptr[0] + ptr[2]38 y = ptr[1] + ptr[3]39 x_ls = [x, x + bs, x + bs, x, x]40 y_ls = [y, y, y + bs, y + bs, y]41 segs.append(np.column_stack([x_ls, y_ls]))42 line_segments = LineCollection(43 segs, linewidths=(.5,), colors=colors, linestyle='solid')44 axis.add_collection(line_segments)45def read_frame(fp, no_swap=0):46 plane = [None, None, None]47 for i in range(3):48 line = fp.readline()49 word_ls = line.split()50 word_ls = [int(item) for item in word_ls]51 rows = word_ls[0]52 cols = word_ls[1]53 line = fp.readline()54 word_ls = line.split()55 word_ls = [int(item) for item in word_ls]56 plane[i] = np.array(word_ls).reshape(rows, cols)57 if i > 0:58 plane[i] = plane[i].repeat(2, axis=0).repeat(2, axis=1)59 plane = np.array(plane)60 if no_swap == 0:61 plane = np.swapaxes(np.swapaxes(plane, 0, 1), 1, 2)62 return plane63def yuv_to_rgb(yuv):64 #mat = np.array([65 # [1.164, 0 , 1.596 ],66 # [1.164, -0.391, -0.813],67 # [1.164, 2.018 , 0 ] ]68 # )69 #c = np.array([[ -16 , -16 , -16 ],70 # [ 0 , -128, -128 ],71 # [ -128, -128, 0 ]])72 mat = np.array([[1, 0, 1.4075], [1, -0.3445, -0.7169], [1, 1.7790, 0]])73 c = np.array([[0, 0, 0], [0, -128, -128], [-128, -128, 0]])74 mat_c = np.dot(mat, c)75 v = np.array([mat_c[0, 0], mat_c[1, 1], mat_c[2, 2]])76 mat = mat.transpose()77 rgb = np.dot(yuv, mat) + v78 rgb = rgb.astype(int)79 rgb = rgb.clip(0, 255)80 return rgb / 255.81def read_feature_score(fp, mv_rows, mv_cols):82 line = fp.readline()83 word_ls = line.split()84 feature_score = np.array([math.log(float(v) + 1, 2) for v in word_ls])85 feature_score = feature_score.reshape(mv_rows, mv_cols)86 return feature_score87def read_mv_mode_arr(fp, mv_rows, mv_cols):88 line = fp.readline()89 word_ls = line.split()90 mv_mode_arr = np.array([int(v) for v in word_ls])91 mv_mode_arr = mv_mode_arr.reshape(mv_rows, mv_cols)92 return mv_mode_arr93def read_frame_dpl_stats(fp):94 line = fp.readline()95 word_ls = line.split()96 frame_idx = int(word_ls[1])97 mi_rows = int(word_ls[3])98 mi_cols = int(word_ls[5])99 bs = int(word_ls[7])100 ref_frame_idx = int(word_ls[9])101 rf_idx = int(word_ls[11])102 gf_frame_offset = int(word_ls[13])103 ref_gf_frame_offset = int(word_ls[15])104 mi_size = bs / 8105 mv_ls = []106 mv_rows = int((math.ceil(mi_rows * 1. / mi_size)))107 mv_cols = int((math.ceil(mi_cols * 1. / mi_size)))108 for i in range(mv_rows * mv_cols):109 line = fp.readline()110 word_ls = line.split()111 row = int(word_ls[0]) * 8.112 col = int(word_ls[1]) * 8.113 mv_row = int(word_ls[2]) / 8.114 mv_col = int(word_ls[3]) / 8.115 mv_ls.append([col, row, mv_col, mv_row])116 mv_ls = np.array(mv_ls)117 feature_score = read_feature_score(fp, mv_rows, mv_cols)118 mv_mode_arr = read_mv_mode_arr(fp, mv_rows, mv_cols)119 img = yuv_to_rgb(read_frame(fp))120 ref = yuv_to_rgb(read_frame(fp))121 return rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, mv_ls, img, ref, bs, feature_score, mv_mode_arr122def read_dpl_stats_file(filename, frame_num=0):123 fp = open(filename)124 line = fp.readline()125 width = 0126 height = 0127 data_ls = []128 while (line):129 if line[0] == '=':130 data_ls.append(read_frame_dpl_stats(fp))131 line = fp.readline()132 if frame_num > 0 and len(data_ls) == frame_num:133 break134 return data_ls135if __name__ == '__main__':136 filename = sys.argv[1]137 data_ls = read_dpl_stats_file(filename, frame_num=5)138 for rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, mv_ls, img, ref, bs, feature_score, mv_mode_arr in data_ls:139 fig, axes = plt.subplots(2, 2)140 axes[0][0].imshow(img)141 draw_mv_ls(axes[0][0], mv_ls)142 draw_pred_block_ls(axes[0][0], mv_ls, bs, mode=0)143 #axes[0].grid(color='k', linestyle='-')144 axes[0][0].set_ylim(img.shape[0], 0)145 axes[0][0].set_xlim(0, img.shape[1])146 if ref is not None:147 axes[0][1].imshow(ref)148 draw_mv_ls(axes[0][1], mv_ls, mode=1)149 draw_pred_block_ls(axes[0][1], mv_ls, bs, mode=1)150 #axes[1].grid(color='k', linestyle='-')151 axes[0][1].set_ylim(ref.shape[0], 0)152 axes[0][1].set_xlim(0, ref.shape[1])153 axes[1][0].imshow(feature_score)154 #feature_score_arr = feature_score.flatten()155 #feature_score_max = feature_score_arr.max()156 #feature_score_min = feature_score_arr.min()157 #step = (feature_score_max - feature_score_min) / 20.158 #feature_score_bins = np.arange(feature_score_min, feature_score_max, step)159 #axes[1][1].hist(feature_score_arr, bins=feature_score_bins)160 im = axes[1][1].imshow(mv_mode_arr)161 #axes[1][1].figure.colorbar(im, ax=axes[1][1])162 print rf_idx, frame_idx, ref_frame_idx, gf_frame_offset, ref_gf_frame_offset, len(mv_ls)163 flatten_mv_mode = mv_mode_arr.flatten()...

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test_transforms.py

Source:test_transforms.py Github

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...54 ifls = [S(533)/672 + 3*I/2, S(23)/224 + I/2, S(1)/672, S(107)/224 - I,55 S(155)/672 + 3*I/2, -S(103)/224 + I/2, -S(377)/672, -S(19)/224 - I]56 assert ifwht(ls) == ifls57 assert fwht(ifls) == ls + [S.Zero]*358 x, y = symbols('x y')59 raises(TypeError, lambda: fwht(x))60 ls = [x, 2*x, 3*x**2, 4*x**3]61 ifls = [x**3 + 3*x**2/4 + 3*x/4,62 -x**3 + 3*x**2/4 - x/4,63 -x**3 - 3*x**2/4 + 3*x/4,64 x**3 - 3*x**2/4 - x/4]65 assert ifwht(ls) == ifls66 assert fwht(ifls) == ls67 ls = [x, y, x**2, y**2, x*y]68 fls = [x**2 + x*y + x + y**2 + y,69 x**2 + x*y + x - y**2 - y,70 -x**2 + x*y + x - y**2 + y,71 -x**2 + x*y + x + y**2 - y,72 x**2 - x*y + x + y**2 + y,73 x**2 - x*y + x - y**2 - y,74 -x**2 - x*y + x - y**2 + y,75 -x**2 - x*y + x + y**2 - y]76 assert fwht(ls) == fls77 assert ifwht(fls) == ls + [S.Zero]*378 ls = list(range(6))79 assert fwht(ls) == [x*8 for x in ifwht(ls)]80def test_mobius_transform():81 assert all(tf(ls, subset=subset) == ls82 for ls in ([], [S(7)/4]) for subset in (True, False)83 for tf in (mobius_transform, inverse_mobius_transform))84 w, x, y, z = symbols('w x y z')85 assert mobius_transform([x, y]) == [x, x + y]86 assert inverse_mobius_transform([x, x + y]) == [x, y]87 assert mobius_transform([x, y], subset=False) == [x + y, y]88 assert inverse_mobius_transform([x + y, y], subset=False) == [x, y]89 assert mobius_transform([w, x, y, z]) == [w, w + x, w + y, w + x + y + z]90 assert inverse_mobius_transform([w, w + x, w + y, w + x + y + z]) == \91 [w, x, y, z]92 assert mobius_transform([w, x, y, z], subset=False) == \93 [w + x + y + z, x + z, y + z, z]94 assert inverse_mobius_transform([w + x + y + z, x + z, y + z, z], subset=False) == \95 [w, x, y, z]96 ls = [S(2)/3, S(6)/7, S(5)/8, 9, S(5)/3 + 7*I]97 mls = [S(2)/3, S(32)/21, S(31)/24, S(1873)/168,98 S(7)/3 + 7*I, S(67)/21 + 7*I, S(71)/24 + 7*I,...

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63010841_Lab9_03.py

Source:63010841_Lab9_03.py Github

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1# รับจำนวนเต็มมา 1 จำนวนแล้วให้แสดงผลดังนี้2# - หาก input ที่รับมานั้นมีการเรียงลำดับจากน้อยไปมาก และไม่มีตัวซ้ำเลยให้แสดงผลว่า "Metadrome"3# - หาก input ที่รับมานั้นมีการเรียงลำดับจากน้อยไปมาก และมีตัวซ้ำให้แสดงผลว่า "Plaindrome"4# - หาก input ที่รับมานั้นมีการเรียงลำดับจากมากไปน้อย และไม่มีตัวซ้ำเลยให้แสดงผลว่า "Katadrome"5# - หาก input ที่รับมานั้นมีการเรียงลำดับจากมากไปน้อย และมีตัวซ้ำให้แสดงผลว่า "Nialpdrome"6# - หาก input ที่รับมานั้นทุกหลักเป็นเลขเดียวกันหมด ให้แสดงผลว่า "Repdrome"7# - หากไม่อยู่ในเงื่อนไขด้านบนเลย ให้แสดงผลว่า "Nondrome"8# ****** ห้ามใช้ Built-in Function ที่เกี่ยวกับ Sort ให้น้องเขียนฟังก์ชัน Sort เอง9def bubble_sort_acend(ls):10 for i in range(len(ls)):11 for j in range(len(ls) - 1):12 if ls[j] > ls[j+1]:13 # Swap14 ls[j], ls[j+1] = ls[j+1], ls[j]15 return ls16def bubble_sort_decend(ls):17 for i in range(len(ls)):18 for j in range(len(ls) - 1):19 if ls[j] < ls[j+1]:20 # Swap21 ls[j], ls[j+1] = ls[j+1], ls[j]22 return ls23 24def ascending_order(ls):25 copy = ls[::]26 copy = bubble_sort_acend(copy)27 return ls == copy28def decending_order(ls):29 copy = ls[::]30 copy = bubble_sort_decend(copy)31 return ls == copy32def doub_check(ls):33 for i in range(len(ls)):34 for j in range(i+1,len(ls)):35 if ls[i] == ls[j]:36 return True37 return False38def same_check(ls):39 for i in range(len(ls)):40 if ls[0] != ls[i]:41 return False42 return True43inp = [int(x) for x in input('Enter Input : ')]44decending_order(inp)45# Metadrome46if ascending_order(inp) and not doub_check(inp) and not same_check(inp):47 print('Metadrome')48# Plaindrome49elif ascending_order(inp) and doub_check(inp) and not same_check(inp):50 print('Plaindrome')51# Katadrome52elif decending_order(inp) and not doub_check(inp) and not same_check(inp):53 print('Katadrome')54# Nialpdrome55elif decending_order(inp) and doub_check(inp) and not same_check(inp):56 print('Nialpdrome')57# Repdrome58elif same_check(inp):59 print('Repdrome')60# Nondrome61else:...

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