Python numpy.uint() Examples
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Example #1
Source File: subsample.py From fastMRI with MIT License | 6 votes |
def __call__(self, shape, seed, offset=None): fraction_low_freqs, acceleration = self.choose_acceleration(seed) num_cols = shape[-2] num_low_freqs = int(round(num_cols * fraction_low_freqs)) # Create the mask mask = np.zeros(num_cols, dtype=np.float32) pad = (num_cols - num_low_freqs + 1) // 2 mask[pad:pad + num_low_freqs] = True # Determine acceleration rate by adjusting for the number of low frequencies adjusted_accel = (acceleration * (num_low_freqs - num_cols)) / (num_low_freqs * acceleration - num_cols) if offset == None: offset = random.randrange(round(adjusted_accel)) accel_samples = np.arange(offset, num_cols - 1, adjusted_accel) accel_samples = np.around(accel_samples).astype(np.uint) mask[accel_samples] = True # Reshape the mask mask_shape = [1 for _ in shape] mask_shape[-2] = num_cols mask = torch.from_numpy(mask.reshape(*mask_shape).astype(np.float32)) return mask, num_low_freqs
Example #2
Source File: main.py From sbb_textline_detection with Apache License 2.0 | 6 votes |
def filter_contours_area_of_image(self, image, contours, hierarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod( image.shape[:2]) and hierarchy[0][jv][3] == -1 : # and hierarchy[0][jv][3]==-1 : found_polygons_early.append( np.array([ [point] for point in polygon.exterior.coords], dtype=np.uint)) jv += 1 return found_polygons_early
Example #3
Source File: main.py From sbb_textline_detection with Apache License 2.0 | 6 votes |
def filter_contours_area_of_image_interiors(self, image, contours, hierarchy, max_area, min_area): found_polygons_early = list() jv = 0 for c in contours: if len(c) < 3: # A polygon cannot have less than 3 points continue polygon = geometry.Polygon([point[0] for point in c]) area = polygon.area if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and \ hierarchy[0][jv][3] != -1: # print(c[0][0][1]) found_polygons_early.append( np.array([point for point in polygon.exterior.coords], dtype=np.uint)) jv += 1 return found_polygons_early
Example #4
Source File: conv_sup_cc_4ch.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #5
Source File: conv_sup_cc_mllsll.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #6
Source File: histogram.py From mars with Apache License 2.0 | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: # pragma: no cover return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #7
Source File: conv_sup_cc_lbp.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #8
Source File: conv_sup_cc.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #9
Source File: conv_sup_cc_4ch_rot.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #10
Source File: conv_sup_cc_lbp.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #11
Source File: conv_sup_cc.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #12
Source File: conv_sup_cc_mllsll.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #13
Source File: test_scaling.py From me-ica with GNU Lesser General Public License v2.1 | 6 votes |
def test_scaling_in_abstract(): # Confirm that, for all ints and uints as input, and all possible outputs, # for any simple way of doing the calculation, the result is near enough for category0, category1 in (('int', 'int'), ('uint', 'int'), ): for in_type in np.sctypes[category0]: for out_type in np.sctypes[category1]: check_int_a2f(in_type, out_type) # Converting floats to integer for category0, category1 in (('float', 'int'), ('float', 'uint'), ('complex', 'int'), ('complex', 'uint'), ): for in_type in np.sctypes[category0]: for out_type in np.sctypes[category1]: check_int_a2f(in_type, out_type)
Example #14
Source File: conv_sup_cc_lbp.py From u24_lymphocyte with BSD 3-Clause "New" or "Revised" License | 6 votes |
def hit_mis(classn, Pr, Tr): print("Hit matrix:"); hit = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 1: hit[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in hit); print("Mis matrix:"); mis = np.zeros(shape=(classn, 2), dtype=np.uint); for i in range(Tr.shape[0]): for truth_ind in range(Tr.shape[1]): if Tr[i][truth_ind] == 0: mis[truth_ind, Pr[i][truth_ind]] += 1; print '\n'.join('\t'.join(str(cell) for cell in row) for row in mis);
Example #15
Source File: GLViewWidget.py From tf-pose with Apache License 2.0 | 6 votes |
def itemsAt(self, region=None): """ Return a list of the items displayed in the region (x, y, w, h) relative to the widget. """ region = (region[0], self.height()-(region[1]+region[3]), region[2], region[3]) #buf = np.zeros(100000, dtype=np.uint) buf = glSelectBuffer(100000) try: glRenderMode(GL_SELECT) glInitNames() glPushName(0) self._itemNames = {} self.paintGL(region=region, useItemNames=True) finally: hits = glRenderMode(GL_RENDER) items = [(h.near, h.names[0]) for h in hits] items.sort(key=lambda i: i[0]) return [self._itemNames[i[1]] for i in items]
Example #16
Source File: data_process.py From FashionAI_KeyPoint_Detection_Challenge_Keras with MIT License | 6 votes |
def rotate_image_with_invrmat(cvmat, rotateAngle): assert (cvmat.dtype == np.uint8) , " only support normalize np.uint in rotate_image_with_invrmat'" ##Make sure cvmat is square? height, width, channel = cvmat.shape center = ( width//2, height//2) rotateMatrix = cv2.getRotationMatrix2D(center, rotateAngle, 1.0) cos, sin = np.abs(rotateMatrix[0,0]), np.abs(rotateMatrix[0, 1]) newH = int((height*sin)+(width*cos)) newW = int((height*cos)+(width*sin)) rotateMatrix[0,2] += (newW/2) - center[0] #x rotateMatrix[1,2] += (newH/2) - center[1] #y # rotate image outMat = cv2.warpAffine(cvmat, rotateMatrix, (newH, newW), borderValue=(128, 128, 128)) # generate inv rotate matrix invRotateMatrix = cv2.invertAffineTransform(rotateMatrix) return (outMat, invRotateMatrix, (width, height))
Example #17
Source File: histograms.py From lambda-packs with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #18
Source File: test_root.py From sonata with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_save_gids(thalamocortical): if os.path.exists('tmp/gid_table.h5'): os.remove('tmp/gid_table.h5') assert(thalamocortical.nodes.has_gids == False) thalamocortical.nodes.generate_gids(file_name='tmp/gid_table.h5') assert(os.path.exists('tmp/gid_table.h5')) gid_h5 = h5py.File('tmp/gid_table.h5', mode='r') assert('gid' in gid_h5) assert(len(gid_h5['gid']) == 9449) assert(np.issubdtype(gid_h5['gid'].dtype, np.uint)) assert('node_id' in gid_h5) assert(len(gid_h5['node_id']) == 9449) assert(np.issubdtype(gid_h5['node_id'].dtype, np.uint)) assert('population' in gid_h5) assert(len(gid_h5['population']) == 9449) print(gid_h5['population']) assert(np.issubdtype(gid_h5['population'].dtype, np.integer)) assert(set(np.unique(gid_h5['population'][...])) == set([0, 1])) assert(set(np.unique(gid_h5['population_names'][...])) == set(['v1', 'lgn']))
Example #19
Source File: histograms.py From pySINDy with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #20
Source File: mnist_training.py From ngraph-python with Apache License 2.0 | 6 votes |
def savetxt(filename, ndarray): dir = os.path.dirname(filename) if not os.path.exists(dir): os.makedirs(dir) if not os.path.isfile(filename): with open(filename, 'w') as f: labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str))) for row in ndarray: row_str = row.astype(str) label_str = labels[row[-1]] feature_str = ' '.join(row_str[:-1]) f.write('|labels {} |features {}\n'.format(label_str, feature_str)) else: print("File already exists", filename)
Example #21
Source File: pauli.py From qiskit-terra with Apache License 2.0 | 6 votes |
def to_spmatrix(self): r""" Convert Pauli to a sparse matrix representation (CSR format). Order is q_{n-1} .... q_0, i.e., $P_{n-1} \otimes ... P_0$ Returns: scipy.sparse.csr_matrix: a sparse matrix with CSR format that represents the pauli. """ _x, _z = self._x, self._z n = 2**len(_x) twos_array = 1 << np.arange(len(_x)) xs = np.array(_x).dot(twos_array) zs = np.array(_z).dot(twos_array) rows = np.arange(n+1, dtype=np.uint) columns = rows ^ xs global_factor = (-1j)**np.dot(np.array(_x, dtype=np.uint), _z) data = global_factor*(-1)**np.mod(_count_set_bits(zs & rows), 2) return sparse.csr_matrix((data, columns, rows), shape=(n, n))
Example #22
Source File: numpy_helpers.py From Amulet-Core with MIT License | 6 votes |
def brute_sort_objects_no_hash(data) -> Tuple[numpy.ndarray, numpy.ndarray]: unique = [] inverse = numpy.zeros(dtype=numpy.uint, shape=len(data)) for i, d in enumerate(data): try: index = unique.index(d) except ValueError: index = len(unique) unique.append(d) inverse[i] = index unique_ = numpy.empty(len(unique), dtype=object) for index, obj in enumerate(unique): unique_[index] = obj return unique_, numpy.array(inverse)
Example #23
Source File: test_distance.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def test_pdist_dtype_equivalence(self): # Tests that the result is not affected by type up-casting eps = 1e-07 tests = [(eo['random-bool-data'], self.valid_upcasts['bool']), (eo['random-uint-data'], self.valid_upcasts['uint']), (eo['random-int-data'], self.valid_upcasts['int']), (eo['random-float32-data'], self.valid_upcasts['float32'])] for metric in _METRICS_NAMES: for test in tests: X1 = test[0][::5, ::2] try: y1 = pdist(X1, metric=metric) except Exception as e: e_cls = e.__class__ if verbose > 2: print(e_cls.__name__) print(e) for new_type in test[1]: X2 = new_type(X1) assert_raises(e_cls, pdist, X2, metric=metric) else: for new_type in test[1]: y2 = pdist(new_type(X1), metric=metric) _assert_within_tol(y1, y2, eps, verbose > 2)
Example #24
Source File: mnist_softmax_cntk.py From ai-gym with MIT License | 6 votes |
def save_as_txt(filename, ndarray): dir = os.path.dirname(filename) if not os.path.exists(dir): os.makedirs(dir) if not os.path.isfile(filename): print("Saving to ", filename, end=" ") with open(filename, 'w') as f: labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str))) for row in ndarray: row_str = row.astype(str) label_str = labels[row[-1]] feature_str = ' '.join(row_str[:-1]) f.write('|labels {} |features {}\n'.format(label_str, feature_str)) else: print("File already exists", filename)
Example #25
Source File: histograms.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #26
Source File: histograms.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #27
Source File: dtypes_test.py From deep_image_model with Apache License 2.0 | 6 votes |
def testNumpyConversion(self): self.assertIs(tf.float32, tf.as_dtype(np.float32)) self.assertIs(tf.float64, tf.as_dtype(np.float64)) self.assertIs(tf.int32, tf.as_dtype(np.int32)) self.assertIs(tf.int64, tf.as_dtype(np.int64)) self.assertIs(tf.uint8, tf.as_dtype(np.uint8)) self.assertIs(tf.uint16, tf.as_dtype(np.uint16)) self.assertIs(tf.int16, tf.as_dtype(np.int16)) self.assertIs(tf.int8, tf.as_dtype(np.int8)) self.assertIs(tf.complex64, tf.as_dtype(np.complex64)) self.assertIs(tf.complex128, tf.as_dtype(np.complex128)) self.assertIs(tf.string, tf.as_dtype(np.object)) self.assertIs(tf.string, tf.as_dtype(np.array(["foo", "bar"]).dtype)) self.assertIs(tf.bool, tf.as_dtype(np.bool)) with self.assertRaises(TypeError): tf.as_dtype(np.dtype([("f1", np.uint), ("f2", np.int32)]))
Example #28
Source File: histograms.py From GraphicDesignPatternByPython with MIT License | 6 votes |
def _unsigned_subtract(a, b): """ Subtract two values where a >= b, and produce an unsigned result This is needed when finding the difference between the upper and lower bound of an int16 histogram """ # coerce to a single type signed_to_unsigned = { np.byte: np.ubyte, np.short: np.ushort, np.intc: np.uintc, np.int_: np.uint, np.longlong: np.ulonglong } dt = np.result_type(a, b) try: dt = signed_to_unsigned[dt.type] except KeyError: return np.subtract(a, b, dtype=dt) else: # we know the inputs are integers, and we are deliberately casting # signed to unsigned return np.subtract(a, b, casting='unsafe', dtype=dt)
Example #29
Source File: sputils.py From GraphicDesignPatternByPython with MIT License | 5 votes |
def get_sum_dtype(dtype): """Mimic numpy's casting for np.sum""" if np.issubdtype(dtype, np.float_): return np.float_ if dtype.kind == 'u' and np.can_cast(dtype, np.uint): return np.uint if np.can_cast(dtype, np.int_): return np.int_ return dtype
Example #30
Source File: deskew.py From lpr with Apache License 2.0 | 5 votes |
def skew_detection(image_gray): h, w = image_gray.shape[:2] eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5) angle_sur = np.zeros(180,np.uint) eigen = eigen.reshape(h, w, 3, 2) flow = eigen[:,:,2] vis = image_gray.copy() vis[:] = (192 + np.uint32(vis)) / 2 d = 12 points = np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2) for x, y in points: vx, vy = np.int32(flow[int(y), int(x)]*d) # cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA) ang = angle(vx,vy) angle_sur[(ang+180)%180] +=1 # torr_bin = 30 angle_sur = angle_sur.astype(np.float) angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min()) angle_sur = filters.gaussian_filter1d(angle_sur,5) skew_v_val = angle_sur[20:180-20].max() skew_v = angle_sur[30:180-30].argmax() + 30 skew_h_A = angle_sur[0:30].max() skew_h_B = angle_sur[150:180].max() skew_h = 0 if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3): if skew_h_A>=skew_h_B: skew_h = angle_sur[0:20].argmax() else: skew_h = - angle_sur[160:180].argmax() return skew_h,skew_v