diff --git a/checkbox-core-snap/common_files/config/wrapper_common_classic b/checkbox-core-snap/common_files/config/wrapper_common_classic index 14cf917f6a..5144801a3a 100644 --- a/checkbox-core-snap/common_files/config/wrapper_common_classic +++ b/checkbox-core-snap/common_files/config/wrapper_common_classic @@ -25,6 +25,8 @@ else append_path LD_LIBRARY_PATH $RUNTIME/lib append_path LD_LIBRARY_PATH $RUNTIME/lib/$ARCH append_path LD_LIBRARY_PATH $RUNTIME/usr/lib/$ARCH + append_path LD_LIBRARY_PATH $RUNTIME/usr/lib/$ARCH/blas + append_path LD_LIBRARY_PATH $RUNTIME/usr/lib/$ARCH/lapack append_path LD_LIBRARY_PATH $RUNTIME/lib/fwts append_path GI_TYPELIB_PATH $RUNTIME/usr/lib/girepository-1.0 append_path GI_TYPELIB_PATH $RUNTIME/usr/lib/$ARCH/girepository-1.0 diff --git a/checkbox-core-snap/series22/snap/snapcraft.yaml b/checkbox-core-snap/series22/snap/snapcraft.yaml index 1773ff74e7..af61e45538 100644 --- a/checkbox-core-snap/series22/snap/snapcraft.yaml +++ b/checkbox-core-snap/series22/snap/snapcraft.yaml @@ -306,6 +306,7 @@ parts: - libasound2 - libcap2-bin - libfdt1 + - libsvm3 - lsb-release - lshw - mesa-utils @@ -323,6 +324,7 @@ parts: - python3-evdev - python3-gi - python3-natsort + - python3-opencv - python3-pil - python3-psutil - python3-pyqrcode diff --git a/checkbox-support/MANIFEST.in b/checkbox-support/MANIFEST.in index a42af311ba..c8bec6c0ba 100644 --- a/checkbox-support/MANIFEST.in +++ b/checkbox-support/MANIFEST.in @@ -7,4 +7,5 @@ recursive-include checkbox_support/parsers/tests/fixtures *.txt recursive-include checkbox_support/parsers/tests/pactl_data *.txt recursive-include checkbox_support/parsers/tests/udevadm_data *.txt *.lsblk recursive-include checkbox_support/snap_utils/tests/asserts_data *.txt +recursive-include checkbox_support/vendor/brisque/models *.txt *.pickle recursive-exclude debian * diff --git a/checkbox-support/checkbox_support/vendor/brisque/CHANGELOG.md b/checkbox-support/checkbox_support/vendor/brisque/CHANGELOG.md new file mode 100644 index 0000000000..c698e2efef --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/CHANGELOG.md @@ -0,0 +1,13 @@ +- 0.0.16 + - Removed url image loading + - Removed scipy dependency + - Changed image reading from skimage to opencv + - Fixed ordered dict creation to work in older versions of python + - Images resized to 640p width for processing + - Using original model +- 0.0.15 + - #9 + - Removed direct image loading instead made it more generic using NdArray + - Moved the loading of the model to init method for faster score processing +- 0.0.14 + - PyTest and Doc Update \ No newline at end of file diff --git a/checkbox-support/checkbox_support/vendor/brisque/LICENSE.txt b/checkbox-support/checkbox_support/vendor/brisque/LICENSE.txt new file mode 100644 index 0000000000..261eeb9e9f --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/LICENSE.txt @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/checkbox-support/checkbox_support/vendor/brisque/__init__.py b/checkbox-support/checkbox_support/vendor/brisque/__init__.py new file mode 100644 index 0000000000..7a26ebb79f --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/__init__.py @@ -0,0 +1 @@ +__version__ = "0.0.16" \ No newline at end of file diff --git a/checkbox-support/checkbox_support/vendor/brisque/brisque.py b/checkbox-support/checkbox_support/vendor/brisque/brisque.py new file mode 100644 index 0000000000..b772e3b484 --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/brisque.py @@ -0,0 +1,210 @@ +import collections +from itertools import chain +import pickle +import os +import math + +import cv2 +import numpy as np + +try: + from .svm import svmutil +except: + from libsvm import svmutil + +from .models import MODEL_PATH + + +class BRISQUE: + def __init__(self): + self.model = os.path.join(MODEL_PATH, "svm.txt") + self.norm = os.path.join(MODEL_PATH, "normalize.pickle") + + # Load in model + self.model = svmutil.svm_load_model(self.model) + with open(self.norm, 'rb') as f: + self.scale_params = pickle.load(f) + + def load_gray_image(self, path): + img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) + + # Resize image to a width of 640 + desired_width = int(640) + height, width = img.shape + aspect_ratio = width / height + desired_height = int(desired_width / aspect_ratio) + resized_image = cv2.resize(img, (desired_width, desired_height), + interpolation=cv2.INTER_NEAREST) + + float_img = np.float64(resized_image) / 255.0 + return float_img + + def score(self, path): + gray_image = self.load_gray_image(path) + # Don't calculate score if image is too homogeneous + if np.std(gray_image) < 1e-2: + return float('nan') + brisque_features = self.calculate_brisque_features( + gray_image, kernel_size=7, sigma=7 / 6) + downscaled_image = cv2.resize(gray_image, None, fx=1 / 2, fy=1 / 2, + interpolation=cv2.INTER_CUBIC) + downscale_brisque_features = self.calculate_brisque_features( + downscaled_image, kernel_size=7, sigma=7 / 6) + brisque_features = np.concatenate( + (brisque_features, downscale_brisque_features)) + + return self.calculate_image_quality_score(brisque_features) + + def normalize_kernel(self, kernel): + return kernel / np.sum(kernel) + + def gaussian_kernel2d(self, n, sigma): + Y, X = np.indices((n, n)) - int(n / 2) + gaussian_kernel = 1 / (2 * np.pi * sigma ** 2) * ( + np.exp(-(X ** 2 + Y ** 2) / (2 * sigma ** 2))) + return self.normalize_kernel(gaussian_kernel) + + def local_mean(self, image, kernel): + return cv2.filter2D(image, -1, cv2.flip(kernel, -1), + borderType=cv2.BORDER_CONSTANT) + + def local_deviation(self, image, local_mean, kernel): + "Vectorized approximation of local deviation" + sigma = image ** 2 + sigma = self.local_mean(sigma, kernel) + return np.sqrt(np.abs(local_mean ** 2 - sigma)) + + def calculate_mscn_coeff(self, image, kernel_size=6, sigma=7 / 6): + C = 1 / 255 + kernel = self.gaussian_kernel2d(kernel_size, sigma=sigma) + local_mean = self.local_mean(image, kernel) + local_var = self.local_deviation(image, local_mean, kernel) + + return (image - local_mean) / (local_var + C) + + def generalized_gaussian_dist(self, x, alpha, sigma): + beta = sigma * np.sqrt(math.gamma(1 / alpha) + / math.gamma(3 / alpha)) + + coefficient = alpha / (2 * beta() * math.gamma(1 / alpha)) + return coefficient * np.exp(-(np.abs(x) / beta) ** alpha) + + def calculate_pair_product_coeff(self, mscn_coeff): + od = collections.OrderedDict() + od['mscn'] = mscn_coeff + od['horizontal'] = mscn_coeff[:, :-1] * mscn_coeff[:, 1:] + od['vertical'] = mscn_coeff[:-1, :] * mscn_coeff[1:, :] + od['main_diagonal'] = (mscn_coeff[:-1, :-1] * + mscn_coeff[1:, 1:]) + od['secondary_diagonal'] = (mscn_coeff[1:, :-1] * + mscn_coeff[:-1, 1:]) + return od + + def asymmetric_generalized_gaussian(self, x, nu, sigma_l, sigma_r): + def beta(sigma): + return sigma * np.sqrt(math.gamma(1 / nu) / + math.gamma(3 / nu)) + + def f_par(x, sigma): + return coefficient * np.exp(-(x / beta(sigma)) ** nu) + + coefficient = nu / ((beta(sigma_l) + beta(sigma_r)) * + math.gamma(1 / nu)) + + return np.where(x < 0, f_par(-x, sigma_l), f_par(x, sigma_r)) + + def asymmetric_generalized_gaussian_fit(self, x): + def estimate_phi(alpha): + numerator = math.gamma(2 / alpha) ** 2 + denominator = math.gamma(1 / alpha) * math.gamma(3 / alpha) + return numerator / denominator + + def estimate_r_hat(x): + size = np.prod(x.shape) + return (np.sum(np.abs(x)) / size) ** 2 / (np.sum(x ** 2) / size) + + def estimate_R_hat(r_hat, gamma): + numerator = (gamma ** 3 + 1) * (gamma + 1) + denominator = (gamma ** 2 + 1) ** 2 + return r_hat * numerator / denominator + + def mean_squares_sum(x, filter=lambda z: z == z): + filtered_values = x[filter(x)] + squares_sum = np.sum(filtered_values ** 2) + return squares_sum / ((filtered_values.shape)) + + def estimate_gamma(x): + left_squares = mean_squares_sum(x, lambda z: z < 0) + right_squares = mean_squares_sum(x, lambda z: z >= 0) + + return np.sqrt(left_squares) / np.sqrt(right_squares) + + def estimate_alpha(x): + r_hat = estimate_r_hat(x) + gamma = estimate_gamma(x) + R_hat = estimate_R_hat(r_hat, gamma) + + # Alternative implementation with scipy.optimize.root + # solution = optimize.root(lambda z: estimate_phi(z) - + # R_hat, [0.2]).x[0] + x_arr = np.arange(0.025, 10 + 0.001, 0.001) + phy_arr = np.asarray([estimate_phi(z) for z in x_arr]) + pos = np.argmin(np.abs(phy_arr - R_hat)) + solution = x_arr[pos] + + return solution + + def estimate_sigma(x, alpha, filter=lambda z: z < 0): + return np.sqrt(mean_squares_sum(x, filter)) + + def estimate_mean(alpha, sigma_l, sigma_r): + return (sigma_r - sigma_l) * constant * (math.gamma(2 / alpha) / + math.gamma(1 / alpha)) + + alpha = estimate_alpha(x) + sigma_l = estimate_sigma(x, alpha, lambda z: z < 0) + sigma_r = estimate_sigma(x, alpha, lambda z: z >= 0) + + constant = np.sqrt(math.gamma(1 / alpha) / math.gamma(3 / alpha)) + mean = estimate_mean(alpha, sigma_l, sigma_r) + + return alpha, mean, sigma_l, sigma_r + + def calculate_brisque_features(self, image, kernel_size=7, sigma=7 / 6): + def calculate_features(coeff_name, coeff): + alpha, mean, sigma_l, sigma_r = ( + self.asymmetric_generalized_gaussian_fit(coeff)) + + if coeff_name == 'mscn': + var = (sigma_l ** 2 + sigma_r ** 2) / 2 + return [alpha, var] + + return [alpha, mean, sigma_l ** 2, sigma_r ** 2] + + mscn_coeff = self.calculate_mscn_coeff(image, kernel_size, sigma) + coeff = self.calculate_pair_product_coeff(mscn_coeff) + + features = [calculate_features(coeff_name=name, coeff=cf) + for name, cf in coeff.items()] + flatten_features = list(chain.from_iterable(features)) + return np.array(flatten_features, dtype=object) + + def scale_features(self, features): + min_ = np.array(self.scale_params['min_'], dtype=np.float64) + max_ = np.array(self.scale_params['max_'], dtype=np.float64) + features = np.array(features, dtype=np.float64) + + return (2.0 / (max_ - min_) * (features - min_)) - 1 + + def calculate_image_quality_score(self, brisque_features): + scaled_brisque_features = self.scale_features(brisque_features) + + x, idx = svmutil.gen_svm_nodearray( + list(scaled_brisque_features), + isKernel=(self.model.param.kernel_type == svmutil.PRECOMPUTED)) + + nr_classifier = 1 + prob_estimates = (svmutil.c_double * nr_classifier)() + + return svmutil.libsvm.svm_predict_probability(self.model, x, + prob_estimates) diff --git a/checkbox-support/checkbox_support/vendor/brisque/models/__init__.py b/checkbox-support/checkbox_support/vendor/brisque/models/__init__.py new file mode 100644 index 0000000000..85cc17f838 --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/models/__init__.py @@ -0,0 +1,3 @@ +import os + +MODEL_PATH = os.path.dirname(os.path.abspath(__file__)) \ No newline at end of file diff --git a/checkbox-support/checkbox_support/vendor/brisque/models/normalize.pickle b/checkbox-support/checkbox_support/vendor/brisque/models/normalize.pickle new file mode 100644 index 0000000000..18ed3adf69 Binary files /dev/null and b/checkbox-support/checkbox_support/vendor/brisque/models/normalize.pickle differ diff --git a/checkbox-support/checkbox_support/vendor/brisque/models/svm.txt b/checkbox-support/checkbox_support/vendor/brisque/models/svm.txt new file mode 100644 index 0000000000..8b5e4912b2 --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/models/svm.txt @@ -0,0 +1,782 @@ +svm_type epsilon_svr +kernel_type rbf +gamma 0.05 +nr_class 2 +total_sv 774 +rho -153.591 +probA 5.71401 +SV +-1024 1:-0.596978 2:-0.136393 3:-0.248933 4:0.432246 5:-0.769052 6:-0.120449 7:-0.265569 8:0.386313 9:-0.746024 10:-0.207071 11:-0.165391 12:-0.0705024 13:-0.592856 14:-0.583769 15:-0.171144 16:-0.0659713 17:-0.589443 18:-0.594489 19:0.674185 20:0.198835 21:0.570048 22:0.368743 23:-0.422326 24:0.232054 25:0.585366 26:0.347393 27:-0.388922 28:0.112603 29:0.677503 30:-0.274459 31:-0.238119 32:-0.168099 33:0.713911 34:-0.266084 35:-0.241827 36:-0.191255 +-1024 1:-0.747257 2:-0.461042 3:-0.509246 4:0.460519 5:-0.924187 6:-0.452938 7:-0.526127 8:0.0919585 9:-0.866725 10:-0.648671 11:-0.427259 12:0.20952 13:-0.849245 14:-0.788123 15:-0.43515 16:0.193888 17:-0.845022 18:-0.798055 19:0.186538 20:-0.121979 21:0.207729 22:0.580158 23:-0.747775 24:-0.0700023 25:0.265854 26:0.0177099 27:-0.568242 28:-0.396922 29:0.336801 30:0.0719453 31:-0.593952 32:-0.429034 33:0.351706 34:-0.10717 35:-0.548696 36:-0.488075 +1024 1:-0.75533 2:-0.574436 3:-0.55192 4:0.379678 5:-0.952804 6:-0.602946 7:-0.57194 8:0.110236 9:-0.908983 10:-0.720716 11:-0.430322 12:0.250062 13:-0.905263 14:-0.852556 15:-0.436685 16:0.304263 17:-0.909137 18:-0.849826 19:0.177945 20:-0.228712 21:0.123188 22:0.453352 23:-0.773433 24:-0.222516 25:0.097561 26:0.0502198 27:-0.640366 28:-0.459302 29:0.328999 30:-0.21165 31:-0.643798 32:-0.644918 33:0.341207 34:-0.0187527 35:-0.678533 36:-0.610199 +1024 1:-0.829642 2:-0.727723 3:-0.694168 4:0.300593 5:-0.987107 6:-0.759054 7:-0.673586 8:0.319375 9:-0.985322 10:-0.804744 11:-0.525268 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a/checkbox-support/checkbox_support/vendor/brisque/svm/__init__.py b/checkbox-support/checkbox_support/vendor/brisque/svm/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/checkbox-support/checkbox_support/vendor/brisque/svm/svm.py b/checkbox-support/checkbox_support/vendor/brisque/svm/svm.py new file mode 100644 index 0000000000..688efee031 --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/svm/svm.py @@ -0,0 +1,444 @@ +#!/usr/bin/env python + +from ctypes import * +from ctypes.util import find_library +from os import path +import sys + +try: + import scipy + from scipy import sparse +except: + scipy = None + sparse = None + +if sys.version_info[0] < 3: + range = xrange + from itertools import izip as zip + +__all__ = ['libsvm', 'svm_problem', 'svm_parameter', + 'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC', + 'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS', + 'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF', + 'SIGMOID', 'c_double', 'svm_model'] + +if sys.platform == 'win32': + try: + dirname = path.dirname(path.abspath(__file__)) + libsvm = CDLL(path.join(dirname, r'..\windows\libsvm.dll')) + except OSError: + raise Exception('LIBSVM library not found.') + +# For unix the prefix 'lib' is not considered. +if find_library('svm'): + libsvm = CDLL(find_library('svm')) +elif find_library('libsvm'): + libsvm = CDLL(find_library('libsvm')) +else: + try: + libsvm = CDLL('libsvm.so.3') + except OSError: + raise Exception('LIBSVM library not found.') + +C_SVC = 0 +NU_SVC = 1 +ONE_CLASS = 2 +EPSILON_SVR = 3 +NU_SVR = 4 + +LINEAR = 0 +POLY = 1 +RBF = 2 +SIGMOID = 3 +PRECOMPUTED = 4 + +PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p) +def print_null(s): + return + +def genFields(names, types): + return list(zip(names, types)) + +def fillprototype(f, restype, argtypes): + f.restype = restype + f.argtypes = argtypes + +class svm_node(Structure): + _names = ["index", "value"] + _types = [c_int, c_double] + _fields_ = genFields(_names, _types) + + def __init__(self, index=-1, value=0): + self.index, self.value = index, value + + def __str__(self): + return '%d:%g' % (self.index, self.value) + +def gen_svm_nodearray(xi, feature_max=None, isKernel=False): + if feature_max: + assert(isinstance(feature_max, int)) + + xi_shift = 0 # ensure correct indices of xi + if scipy and isinstance(xi, tuple) and len(xi) == 2\ + and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector + if not isKernel: + index_range = xi[0] + 1 # index starts from 1 + else: + index_range = xi[0] # index starts from 0 for precomputed kernel + if feature_max: + index_range = index_range[scipy.where(index_range <= feature_max)] + elif scipy and isinstance(xi, scipy.ndarray): + if not isKernel: + xi_shift = 1 + index_range = xi.nonzero()[0] + 1 # index starts from 1 + else: + index_range = scipy.arange(0, len(xi)) # index starts from 0 for precomputed kernel + if feature_max: + index_range = index_range[scipy.where(index_range <= feature_max)] + elif isinstance(xi, (dict, list, tuple)): + if isinstance(xi, dict): + index_range = xi.keys() + elif isinstance(xi, (list, tuple)): + if not isKernel: + xi_shift = 1 + index_range = range(1, len(xi) + 1) # index starts from 1 + else: + index_range = range(0, len(xi)) # index starts from 0 for precomputed kernel + + if feature_max: + index_range = filter(lambda j: j <= feature_max, index_range) + if not isKernel: + index_range = filter(lambda j:xi[j-xi_shift] != 0, index_range) + + index_range = sorted(index_range) + else: + raise TypeError('xi should be a dictionary, list, tuple, 1-d numpy array, or tuple of (index, data)') + + ret = (svm_node*(len(index_range)+1))() + ret[-1].index = -1 + + if scipy and isinstance(xi, tuple) and len(xi) == 2\ + and isinstance(xi[0], scipy.ndarray) and isinstance(xi[1], scipy.ndarray): # for a sparse vector + for idx, j in enumerate(index_range): + ret[idx].index = j + ret[idx].value = (xi[1])[idx] + else: + for idx, j in enumerate(index_range): + ret[idx].index = j + ret[idx].value = xi[j - xi_shift] + + max_idx = 0 + if len(index_range) > 0: + max_idx = index_range[-1] + return ret, max_idx + +try: + from numba import jit + jit_enabled = True +except: + jit = lambda x: x + jit_enabled = False + +@jit +def csr_to_problem_jit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start): + for i in range(l): + b1,e1 = x_rowptr[i], x_rowptr[i+1] + b2,e2 = prob_rowptr[i], prob_rowptr[i+1]-1 + for j in range(b1,e1): + prob_ind[j-b1+b2] = x_ind[j]+indx_start + prob_val[j-b1+b2] = x_val[j] +def csr_to_problem_nojit(l, x_val, x_ind, x_rowptr, prob_val, prob_ind, prob_rowptr, indx_start): + for i in range(l): + x_slice = slice(x_rowptr[i], x_rowptr[i+1]) + prob_slice = slice(prob_rowptr[i], prob_rowptr[i+1]-1) + prob_ind[prob_slice] = x_ind[x_slice]+indx_start + prob_val[prob_slice] = x_val[x_slice] + +def csr_to_problem(x, prob, isKernel): + if not x.has_sorted_indices: + x.sort_indices() + + # Extra space for termination node and (possibly) bias term + x_space = prob.x_space = scipy.empty((x.nnz+x.shape[0]), dtype=svm_node) + prob.rowptr = x.indptr.copy() + prob.rowptr[1:] += scipy.arange(1,x.shape[0]+1) + prob_ind = x_space["index"] + prob_val = x_space["value"] + prob_ind[:] = -1 + if not isKernel: + indx_start = 1 # index starts from 1 + else: + indx_start = 0 # index starts from 0 for precomputed kernel + if jit_enabled: + csr_to_problem_jit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start) + else: + csr_to_problem_nojit(x.shape[0], x.data, x.indices, x.indptr, prob_val, prob_ind, prob.rowptr, indx_start) + +class svm_problem(Structure): + _names = ["l", "y", "x"] + _types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))] + _fields_ = genFields(_names, _types) + + def __init__(self, y, x, isKernel=False): + if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))): + raise TypeError("type of y: {0} is not supported!".format(type(y))) + + if isinstance(x, (list, tuple)): + if len(y) != len(x): + raise ValueError("len(y) != len(x)") + elif scipy != None and isinstance(x, (scipy.ndarray, sparse.spmatrix)): + if len(y) != x.shape[0]: + raise ValueError("len(y) != len(x)") + if isinstance(x, scipy.ndarray): + x = scipy.ascontiguousarray(x) # enforce row-major + if isinstance(x, sparse.spmatrix): + x = x.tocsr() + pass + else: + raise TypeError("type of x: {0} is not supported!".format(type(x))) + self.l = l = len(y) + + max_idx = 0 + x_space = self.x_space = [] + if scipy != None and isinstance(x, sparse.csr_matrix): + csr_to_problem(x, self, isKernel) + max_idx = x.shape[1] + else: + for i, xi in enumerate(x): + tmp_xi, tmp_idx = gen_svm_nodearray(xi,isKernel=isKernel) + x_space += [tmp_xi] + max_idx = max(max_idx, tmp_idx) + self.n = max_idx + + self.y = (c_double * l)() + if scipy != None and isinstance(y, scipy.ndarray): + scipy.ctypeslib.as_array(self.y, (self.l,))[:] = y + else: + for i, yi in enumerate(y): self.y[i] = yi + + self.x = (POINTER(svm_node) * l)() + if scipy != None and isinstance(x, sparse.csr_matrix): + base = addressof(self.x_space.ctypes.data_as(POINTER(svm_node))[0]) + x_ptr = cast(self.x, POINTER(c_uint64)) + x_ptr = scipy.ctypeslib.as_array(x_ptr,(self.l,)) + x_ptr[:] = self.rowptr[:-1]*sizeof(svm_node)+base + else: + for i, xi in enumerate(self.x_space): self.x[i] = xi + +class svm_parameter(Structure): + _names = ["svm_type", "kernel_type", "degree", "gamma", "coef0", + "cache_size", "eps", "C", "nr_weight", "weight_label", "weight", + "nu", "p", "shrinking", "probability"] + _types = [c_int, c_int, c_int, c_double, c_double, + c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double), + c_double, c_double, c_int, c_int] + _fields_ = genFields(_names, _types) + + def __init__(self, options = None): + if options == None: + options = '' + self.parse_options(options) + + def __str__(self): + s = '' + attrs = svm_parameter._names + list(self.__dict__.keys()) + values = map(lambda attr: getattr(self, attr), attrs) + for attr, val in zip(attrs, values): + s += (' %s: %s\n' % (attr, val)) + s = s.strip() + + return s + + def set_to_default_values(self): + self.svm_type = C_SVC; + self.kernel_type = RBF + self.degree = 3 + self.gamma = 0 + self.coef0 = 0 + self.nu = 0.5 + self.cache_size = 100 + self.C = 1 + self.eps = 0.001 + self.p = 0.1 + self.shrinking = 1 + self.probability = 0 + self.nr_weight = 0 + self.weight_label = None + self.weight = None + self.cross_validation = False + self.nr_fold = 0 + self.print_func = cast(None, PRINT_STRING_FUN) + + def parse_options(self, options): + if isinstance(options, list): + argv = options + elif isinstance(options, str): + argv = options.split() + else: + raise TypeError("arg 1 should be a list or a str.") + self.set_to_default_values() + self.print_func = cast(None, PRINT_STRING_FUN) + weight_label = [] + weight = [] + + i = 0 + while i < len(argv): + if argv[i] == "-s": + i = i + 1 + self.svm_type = int(argv[i]) + elif argv[i] == "-t": + i = i + 1 + self.kernel_type = int(argv[i]) + elif argv[i] == "-d": + i = i + 1 + self.degree = int(argv[i]) + elif argv[i] == "-g": + i = i + 1 + self.gamma = float(argv[i]) + elif argv[i] == "-r": + i = i + 1 + self.coef0 = float(argv[i]) + elif argv[i] == "-n": + i = i + 1 + self.nu = float(argv[i]) + elif argv[i] == "-m": + i = i + 1 + self.cache_size = float(argv[i]) + elif argv[i] == "-c": + i = i + 1 + self.C = float(argv[i]) + elif argv[i] == "-e": + i = i + 1 + self.eps = float(argv[i]) + elif argv[i] == "-p": + i = i + 1 + self.p = float(argv[i]) + elif argv[i] == "-h": + i = i + 1 + self.shrinking = int(argv[i]) + elif argv[i] == "-b": + i = i + 1 + self.probability = int(argv[i]) + elif argv[i] == "-q": + self.print_func = PRINT_STRING_FUN(print_null) + elif argv[i] == "-v": + i = i + 1 + self.cross_validation = 1 + self.nr_fold = int(argv[i]) + if self.nr_fold < 2: + raise ValueError("n-fold cross validation: n must >= 2") + elif argv[i].startswith("-w"): + i = i + 1 + self.nr_weight += 1 + weight_label += [int(argv[i-1][2:])] + weight += [float(argv[i])] + else: + raise ValueError("Wrong options") + i += 1 + + libsvm.svm_set_print_string_function(self.print_func) + self.weight_label = (c_int*self.nr_weight)() + self.weight = (c_double*self.nr_weight)() + for i in range(self.nr_weight): + self.weight[i] = weight[i] + self.weight_label[i] = weight_label[i] + +class svm_model(Structure): + _names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 'rho', + 'probA', 'probB', 'sv_indices', 'label', 'nSV', 'free_sv'] + _types = [svm_parameter, c_int, c_int, POINTER(POINTER(svm_node)), + POINTER(POINTER(c_double)), POINTER(c_double), + POINTER(c_double), POINTER(c_double), POINTER(c_int), + POINTER(c_int), POINTER(c_int), c_int] + _fields_ = genFields(_names, _types) + + def __init__(self): + self.__createfrom__ = 'python' + + def __del__(self): + # free memory created by C to avoid memory leak + if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C': + libsvm.svm_free_and_destroy_model(pointer(pointer(self))) + + def get_svm_type(self): + return libsvm.svm_get_svm_type(self) + + def get_nr_class(self): + return libsvm.svm_get_nr_class(self) + + def get_svr_probability(self): + return libsvm.svm_get_svr_probability(self) + + def get_labels(self): + nr_class = self.get_nr_class() + labels = (c_int * nr_class)() + libsvm.svm_get_labels(self, labels) + return labels[:nr_class] + + def get_sv_indices(self): + total_sv = self.get_nr_sv() + sv_indices = (c_int * total_sv)() + libsvm.svm_get_sv_indices(self, sv_indices) + return sv_indices[:total_sv] + + def get_nr_sv(self): + return libsvm.svm_get_nr_sv(self) + + def is_probability_model(self): + return (libsvm.svm_check_probability_model(self) == 1) + + def get_sv_coef(self): + return [tuple(self.sv_coef[j][i] for j in range(self.nr_class - 1)) + for i in range(self.l)] + + def get_SV(self): + result = [] + for sparse_sv in self.SV[:self.l]: + row = dict() + + i = 0 + while True: + if sparse_sv[i].index == -1: + break + row[sparse_sv[i].index] = sparse_sv[i].value + i += 1 + + result.append(row) + return result + +def toPyModel(model_ptr): + """ + toPyModel(model_ptr) -> svm_model + + Convert a ctypes POINTER(svm_model) to a Python svm_model + """ + if bool(model_ptr) == False: + raise ValueError("Null pointer") + m = model_ptr.contents + m.__createfrom__ = 'C' + return m + +fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)]) +fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)]) + +fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)]) +fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p]) + +fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)]) +fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)]) +fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)]) +fillprototype(libsvm.svm_get_sv_indices, None, [POINTER(svm_model), POINTER(c_int)]) +fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)]) +fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)]) + +fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)]) +fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)]) +fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)]) + +fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)]) +fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))]) +fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)]) + +fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)]) +fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)]) +fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN]) diff --git a/checkbox-support/checkbox_support/vendor/brisque/svm/svmutil.py b/checkbox-support/checkbox_support/vendor/brisque/svm/svmutil.py new file mode 100644 index 0000000000..288a434739 --- /dev/null +++ b/checkbox-support/checkbox_support/vendor/brisque/svm/svmutil.py @@ -0,0 +1,442 @@ +#!/usr/bin/env python + +import os, sys +sys.path = [os.path.dirname(os.path.abspath(__file__))] + sys.path +from svm import * +from svm import __all__ as svm_all +from svm import scipy, sparse + +if sys.version_info[0] < 3: + range = xrange + from itertools import izip as zip + _cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s) +else: + _cstr = lambda s: bytes(s, "utf-8") + + +########################################################### +#### Debian: merge commonutil.py into this svmutil.py ##### +########################################################### + +from array import array +import sys + +try: + import scipy + from scipy import sparse +except: + scipy = None + sparse = None + +common_all = ['svm_read_problem', 'evaluations', 'csr_find_scale_param', 'csr_scale'] + +def svm_read_problem(data_file_name, return_scipy=False): + """ + svm_read_problem(data_file_name, return_scipy=False) -> [y, x], y: list, x: list of dictionary + svm_read_problem(data_file_name, return_scipy=True) -> [y, x], y: ndarray, x: csr_matrix + + Read LIBSVM-format data from data_file_name and return labels y + and data instances x. + """ + if scipy != None and return_scipy: + prob_y = array('d') + prob_x = array('d') + row_ptr = array('l', [0]) + col_idx = array('l') + else: + prob_y = [] + prob_x = [] + row_ptr = [0] + col_idx = [] + indx_start = 1 + for i, line in enumerate(open(data_file_name)): + line = line.split(None, 1) + # In case an instance with all zero features + if len(line) == 1: line += [''] + label, features = line + prob_y.append(float(label)) + if scipy != None and return_scipy: + nz = 0 + for e in features.split(): + ind, val = e.split(":") + if ind == '0': + indx_start = 0 + val = float(val) + if val != 0: + col_idx.append(int(ind)-indx_start) + prob_x.append(val) + nz += 1 + row_ptr.append(row_ptr[-1]+nz) + else: + xi = {} + for e in features.split(): + ind, val = e.split(":") + xi[int(ind)] = float(val) + prob_x += [xi] + if scipy != None and return_scipy: + prob_y = scipy.frombuffer(prob_y, dtype='d') + prob_x = scipy.frombuffer(prob_x, dtype='d') + col_idx = scipy.frombuffer(col_idx, dtype='l') + row_ptr = scipy.frombuffer(row_ptr, dtype='l') + prob_x = sparse.csr_matrix((prob_x, col_idx, row_ptr)) + return (prob_y, prob_x) + +def evaluations_scipy(ty, pv): + """ + evaluations_scipy(ty, pv) -> (ACC, MSE, SCC) + ty, pv: ndarray + + Calculate accuracy, mean squared error and squared correlation coefficient + using the true values (ty) and predicted values (pv). + """ + if not (scipy != None and isinstance(ty, scipy.ndarray) and isinstance(pv, scipy.ndarray)): + raise TypeError("type of ty and pv must be ndarray") + if len(ty) != len(pv): + raise ValueError("len(ty) must be equal to len(pv)") + ACC = 100.0*(ty == pv).mean() + MSE = ((ty - pv)**2).mean() + l = len(ty) + sumv = pv.sum() + sumy = ty.sum() + sumvy = (pv*ty).sum() + sumvv = (pv*pv).sum() + sumyy = (ty*ty).sum() + with scipy.errstate(all = 'raise'): + try: + SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy)) + except: + SCC = float('nan') + return (float(ACC), float(MSE), float(SCC)) + +def evaluations(ty, pv, useScipy = True): + """ + evaluations(ty, pv, useScipy) -> (ACC, MSE, SCC) + ty, pv: list, tuple or ndarray + useScipy: convert ty, pv to ndarray, and use scipy functions for the evaluation + + Calculate accuracy, mean squared error and squared correlation coefficient + using the true values (ty) and predicted values (pv). + """ + if scipy != None and useScipy: + return evaluations_scipy(scipy.asarray(ty), scipy.asarray(pv)) + if len(ty) != len(pv): + raise ValueError("len(ty) must be equal to len(pv)") + total_correct = total_error = 0 + sumv = sumy = sumvv = sumyy = sumvy = 0 + for v, y in zip(pv, ty): + if y == v: + total_correct += 1 + total_error += (v-y)*(v-y) + sumv += v + sumy += y + sumvv += v*v + sumyy += y*y + sumvy += v*y + l = len(ty) + ACC = 100.0*total_correct/l + MSE = total_error/l + try: + SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy)) + except: + SCC = float('nan') + return (float(ACC), float(MSE), float(SCC)) + +def csr_find_scale_param(x, lower=-1, upper=1): + assert isinstance(x, sparse.csr_matrix) + assert lower < upper + l, n = x.shape + feat_min = x.min(axis=0).toarray().flatten() + feat_max = x.max(axis=0).toarray().flatten() + coef = (feat_max - feat_min) / (upper - lower) + coef[coef != 0] = 1.0 / coef[coef != 0] + + # (x - ones(l,1) * feat_min') * diag(coef) + lower + # = x * diag(coef) - ones(l, 1) * (feat_min' * diag(coef)) + lower + # = x * diag(coef) + ones(l, 1) * (-feat_min' * diag(coef) + lower) + # = x * diag(coef) + ones(l, 1) * offset' + offset = -feat_min * coef + lower + offset[coef == 0] = 0 + + if sum(offset != 0) * l > 3 * x.getnnz(): + print( + "WARNING: The #nonzeros of the scaled data is at least 2 times larger than the original one.\n" + "If feature values are non-negative and sparse, set lower=0 rather than the default lower=-1.", + file=sys.stderr) + + return {'coef':coef, 'offset':offset} + +def csr_scale(x, scale_param): + assert isinstance(x, sparse.csr_matrix) + + offset = scale_param['offset'] + coef = scale_param['coef'] + assert len(coef) == len(offset) + + l, n = x.shape + + if not n == len(coef): + print("WARNING: The dimension of scaling parameters and feature number do not match.", file=sys.stderr) + coef = resize(coef, n) + offset = resize(offset, n) + + # scaled_x = x * diag(coef) + ones(l, 1) * offset' + offset = sparse.csr_matrix(offset.reshape(1, n)) + offset = sparse.vstack([offset] * l, format='csr', dtype=x.dtype) + scaled_x = x.dot(sparse.diags(coef, 0, shape=(n, n))) + offset + + if scaled_x.getnnz() > x.getnnz(): + print( + "WARNING: original #nonzeros %d\n" % x.getnnz() + + " > new #nonzeros %d\n" % scaled_x.getnnz() + + "If feature values are non-negative and sparse, get scale_param by setting lower=0 rather than the default lower=-1.", + file=sys.stderr) + + return scaled_x + +##################################### +#### End of merged commonutil.py #### +##################################### + + +__all__ = ['svm_load_model', 'svm_predict', 'svm_save_model', 'svm_train'] + svm_all + common_all + + +def svm_load_model(model_file_name): + """ + svm_load_model(model_file_name) -> model + + Load a LIBSVM model from model_file_name and return. + """ + model = libsvm.svm_load_model(_cstr(model_file_name)) + if not model: + print("can't open model file %s" % model_file_name) + return None + model = toPyModel(model) + return model + +def svm_save_model(model_file_name, model): + """ + svm_save_model(model_file_name, model) -> None + + Save a LIBSVM model to the file model_file_name. + """ + libsvm.svm_save_model(_cstr(model_file_name), model) + +def svm_train(arg1, arg2=None, arg3=None): + """ + svm_train(y, x [, options]) -> model | ACC | MSE + + y: a list/tuple/ndarray of l true labels (type must be int/double). + + x: 1. a list/tuple of l training instances. Feature vector of + each training instance is a list/tuple or dictionary. + + 2. an l * n numpy ndarray or scipy spmatrix (n: number of features). + + svm_train(prob [, options]) -> model | ACC | MSE + svm_train(prob, param) -> model | ACC| MSE + + Train an SVM model from data (y, x) or an svm_problem prob using + 'options' or an svm_parameter param. + If '-v' is specified in 'options' (i.e., cross validation) + either accuracy (ACC) or mean-squared error (MSE) is returned. + options: + -s svm_type : set type of SVM (default 0) + 0 -- C-SVC (multi-class classification) + 1 -- nu-SVC (multi-class classification) + 2 -- one-class SVM + 3 -- epsilon-SVR (regression) + 4 -- nu-SVR (regression) + -t kernel_type : set type of kernel function (default 2) + 0 -- linear: u'*v + 1 -- polynomial: (gamma*u'*v + coef0)^degree + 2 -- radial basis function: exp(-gamma*|u-v|^2) + 3 -- sigmoid: tanh(gamma*u'*v + coef0) + 4 -- precomputed kernel (kernel values in training_set_file) + -d degree : set degree in kernel function (default 3) + -g gamma : set gamma in kernel function (default 1/num_features) + -r coef0 : set coef0 in kernel function (default 0) + -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) + -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) + -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) + -m cachesize : set cache memory size in MB (default 100) + -e epsilon : set tolerance of termination criterion (default 0.001) + -h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1) + -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) + -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1) + -v n: n-fold cross validation mode + -q : quiet mode (no outputs) + """ + prob, param = None, None + if isinstance(arg1, (list, tuple)) or (scipy and isinstance(arg1, scipy.ndarray)): + assert isinstance(arg2, (list, tuple)) or (scipy and isinstance(arg2, (scipy.ndarray, sparse.spmatrix))) + y, x, options = arg1, arg2, arg3 + param = svm_parameter(options) + prob = svm_problem(y, x, isKernel=(param.kernel_type == PRECOMPUTED)) + elif isinstance(arg1, svm_problem): + prob = arg1 + if isinstance(arg2, svm_parameter): + param = arg2 + else: + param = svm_parameter(arg2) + if prob == None or param == None: + raise TypeError("Wrong types for the arguments") + + if param.kernel_type == PRECOMPUTED: + for i in range(prob.l): + xi = prob.x[i] + idx, val = xi[0].index, xi[0].value + if idx != 0: + raise ValueError('Wrong input format: first column must be 0:sample_serial_number') + if val <= 0 or val > prob.n: + raise ValueError('Wrong input format: sample_serial_number out of range') + + if param.gamma == 0 and prob.n > 0: + param.gamma = 1.0 / prob.n + libsvm.svm_set_print_string_function(param.print_func) + err_msg = libsvm.svm_check_parameter(prob, param) + if err_msg: + raise ValueError('Error: %s' % err_msg) + + if param.cross_validation: + l, nr_fold = prob.l, param.nr_fold + target = (c_double * l)() + libsvm.svm_cross_validation(prob, param, nr_fold, target) + ACC, MSE, SCC = evaluations(prob.y[:l], target[:l]) + if param.svm_type in [EPSILON_SVR, NU_SVR]: + print("Cross Validation Mean squared error = %g" % MSE) + print("Cross Validation Squared correlation coefficient = %g" % SCC) + return MSE + else: + print("Cross Validation Accuracy = %g%%" % ACC) + return ACC + else: + m = libsvm.svm_train(prob, param) + m = toPyModel(m) + + # If prob is destroyed, data including SVs pointed by m can remain. + m.x_space = prob.x_space + return m + +def svm_predict(y, x, m, options=""): + """ + svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals) + + y: a list/tuple/ndarray of l true labels (type must be int/double). + It is used for calculating the accuracy. Use [] if true labels are + unavailable. + + x: 1. a list/tuple of l training instances. Feature vector of + each training instance is a list/tuple or dictionary. + + 2. an l * n numpy ndarray or scipy spmatrix (n: number of features). + + Predict data (y, x) with the SVM model m. + options: + -b probability_estimates: whether to predict probability estimates, + 0 or 1 (default 0); for one-class SVM only 0 is supported. + -q : quiet mode (no outputs). + + The return tuple contains + p_labels: a list of predicted labels + p_acc: a tuple including accuracy (for classification), mean-squared + error, and squared correlation coefficient (for regression). + p_vals: a list of decision values or probability estimates (if '-b 1' + is specified). If k is the number of classes, for decision values, + each element includes results of predicting k(k-1)/2 binary-class + SVMs. For probabilities, each element contains k values indicating + the probability that the testing instance is in each class. + Note that the order of classes here is the same as 'model.label' + field in the model structure. + """ + + def info(s): + print(s) + + if scipy and isinstance(x, scipy.ndarray): + x = scipy.ascontiguousarray(x) # enforce row-major + elif sparse and isinstance(x, sparse.spmatrix): + x = x.tocsr() + elif not isinstance(x, (list, tuple)): + raise TypeError("type of x: {0} is not supported!".format(type(x))) + + if (not isinstance(y, (list, tuple))) and (not (scipy and isinstance(y, scipy.ndarray))): + raise TypeError("type of y: {0} is not supported!".format(type(y))) + + predict_probability = 0 + argv = options.split() + i = 0 + while i < len(argv): + if argv[i] == '-b': + i += 1 + predict_probability = int(argv[i]) + elif argv[i] == '-q': + info = print_null + else: + raise ValueError("Wrong options") + i+=1 + + svm_type = m.get_svm_type() + is_prob_model = m.is_probability_model() + nr_class = m.get_nr_class() + pred_labels = [] + pred_values = [] + + if scipy and isinstance(x, sparse.spmatrix): + nr_instance = x.shape[0] + else: + nr_instance = len(x) + + if predict_probability: + if not is_prob_model: + raise ValueError("Model does not support probabiliy estimates") + + if svm_type in [NU_SVR, EPSILON_SVR]: + info("Prob. model for test data: target value = predicted value + z,\n" + "z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability()); + nr_class = 0 + + prob_estimates = (c_double * nr_class)() + for i in range(nr_instance): + if scipy and isinstance(x, sparse.spmatrix): + indslice = slice(x.indptr[i], x.indptr[i+1]) + xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED)) + else: + xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED)) + label = libsvm.svm_predict_probability(m, xi, prob_estimates) + values = prob_estimates[:nr_class] + pred_labels += [label] + pred_values += [values] + else: + if is_prob_model: + info("Model supports probability estimates, but disabled in predicton.") + if svm_type in (ONE_CLASS, EPSILON_SVR, NU_SVC): + nr_classifier = 1 + else: + nr_classifier = nr_class*(nr_class-1)//2 + dec_values = (c_double * nr_classifier)() + for i in range(nr_instance): + if scipy and isinstance(x, sparse.spmatrix): + indslice = slice(x.indptr[i], x.indptr[i+1]) + xi, idx = gen_svm_nodearray((x.indices[indslice], x.data[indslice]), isKernel=(m.param.kernel_type == PRECOMPUTED)) + else: + xi, idx = gen_svm_nodearray(x[i], isKernel=(m.param.kernel_type == PRECOMPUTED)) + label = libsvm.svm_predict_values(m, xi, dec_values) + if(nr_class == 1): + values = [1] + else: + values = dec_values[:nr_classifier] + pred_labels += [label] + pred_values += [values] + + if len(y) == 0: + y = [0] * nr_instance + ACC, MSE, SCC = evaluations(y, pred_labels) + + if svm_type in [EPSILON_SVR, NU_SVR]: + info("Mean squared error = %g (regression)" % MSE) + info("Squared correlation coefficient = %g (regression)" % SCC) + else: + info("Accuracy = %g%% (%d/%d) (classification)" % (ACC, int(round(nr_instance*ACC/100)), nr_instance)) + + return pred_labels, (ACC, MSE, SCC), pred_values diff --git a/providers/base/bin/camera_quality_test.py b/providers/base/bin/camera_quality_test.py new file mode 100755 index 0000000000..633c7bd5ee --- /dev/null +++ b/providers/base/bin/camera_quality_test.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +# +# This file is part of Checkbox. +# +# Copyright 2008-2023 Canonical Ltd. +# Written by: +# Fernando Bravo +# +# Checkbox is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License version 3, +# as published by the Free Software Foundation. +# +# Checkbox is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with Checkbox. If not, see . +# + +import argparse +import time +import sys + +import cv2 +from numpy import isnan + +from checkbox_support.vendor.brisque.brisque import BRISQUE +from tempfile import NamedTemporaryFile + +THRESHOLD = 60 + + +def get_image_from_device(device: str): + # Set the video device + index = int(device.replace("video", "")) + cam = cv2.VideoCapture(index) + + if not cam.isOpened(): + print("Cannot open the selected device", file=sys.stderr) + return None + + # Discard the frames for 4 seconds + tmax = time.time() + 4 + while time.time() < tmax: + if not cam.grab(): + print("Cannot read from the selected device", file=sys.stderr) + return None + + # Get the image + result, image = cam.read() + cam.release() + if not result: + print("Cannot read from the selected device", file=sys.stderr) + return None + + return image + + +def brisque(device: str = "video0", file: str = "", save: bool = False): + """ + Captures an image to a file and computes the quality using the + Blinded/Unreferenced Spatial Image Quality Evaluator (BRISQUE). If the + score is below a certain threshold, the test passes. + """ + + brisque = BRISQUE() + if file: + score = brisque.score(file) + + else: + image = get_image_from_device(device) + if image is None: + return 1 + + # Create a temporary file + f = NamedTemporaryFile(prefix='camera_test_brisque_%s_' % device, + suffix='.jpg', delete=not save) + cv2.imwrite(f.name, image) + if save: + print("Image saved to %s" % f.name) + + # Compute the BRISQUE score + score = brisque.score(f.name) + f.close() + + if isnan(score): + print("Unable to compute BRISQUE score", file=sys.stderr) + return 1 + elif score > THRESHOLD: + print("The BRISQUE score is too high: %s" % score, file=sys.stderr) + return 1 + + print("BRISQUE score: %s" % score) + return 0 + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Run the image quality test") + parser.add_argument("-d", "--device", default="video0", + help="Device for the webcam to use") + parser.add_argument("-f", "--file", default="", + help="Parse a file instead of a device") + parser.add_argument("-s", "--save", action="store_true", + help="Keep the image file after the test") + args = parser.parse_args() + + sys.exit(brisque(args.device, args.file, args.save)) diff --git a/providers/base/data/images/image_quality_bad.jpg b/providers/base/data/images/image_quality_bad.jpg new file mode 100644 index 0000000000..dea1b8d5d7 Binary files /dev/null and b/providers/base/data/images/image_quality_bad.jpg differ diff --git a/providers/base/data/images/image_quality_good.jpg b/providers/base/data/images/image_quality_good.jpg new file mode 100644 index 0000000000..443f37e73a Binary files /dev/null and b/providers/base/data/images/image_quality_good.jpg differ diff --git a/providers/base/data/images/image_quality_plain.jpg b/providers/base/data/images/image_quality_plain.jpg new file mode 100644 index 0000000000..9405fc6e42 Binary files /dev/null and b/providers/base/data/images/image_quality_plain.jpg differ diff --git a/providers/base/tests/test_camera_quality.py b/providers/base/tests/test_camera_quality.py new file mode 100644 index 0000000000..fc2940ea80 --- /dev/null +++ b/providers/base/tests/test_camera_quality.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +# +# This file is part of Checkbox. +# +# Copyright 2023 Canonical Ltd. +# Written by: +# Fernando Bravo +# +# Checkbox is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License version 3, +# as published by the Free Software Foundation. +# +# Checkbox is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with Checkbox. If not, see . +# + +import os +import unittest +from unittest.mock import patch + +import cv2 + +from bin.camera_quality_test import brisque + +default_dir = os.path.join(os.path.dirname(__file__), '../data') +data_dir = os.getenv('PLAINBOX_PROVIDER_DATA', default=default_dir) + + +class CameraQualityTests(unittest.TestCase): + """This class provides test cases for the camera_quality_test module.""" + + # Setup the patch for all the tests + def setUp(self): + self.patcher = patch('cv2.VideoCapture') + self.mock_capture = self.patcher.start() + + def tearDown(self): + self.patcher.stop() + + def test_device_not_opened(self): + """ + The test should fail if the camera device is not opened. + """ + + # Set the mock + self.mock_capture.return_value.isOpened.return_value = False + + assert brisque() == 1 + + def test_grab_not_available(self): + """ + The test should fail if the camera device can't grab an image. + """ + + # Set the mock + self.mock_capture.return_value.isOpened.return_value = True + self.mock_capture.return_value.grab.return_value = False + + assert brisque() == 1 + + def test_image_not_read(self): + """ + The test should fail if the camera device can't read the image. + """ + + # Set the mock + self.mock_capture.return_value.isOpened.return_value = True + self.mock_capture.return_value.grab.return_value = True + self.mock_capture.return_value.read.return_value = (False, None) + + assert brisque() == 1 + + def test_good_image_from_camera(self): + """ + Check if the test passes with a valid image. + """ + # Set the mock + img_path = os.path.join(data_dir, "images/image_quality_good.jpg") + img = cv2.imread(img_path) + self.mock_capture.return_value.isOpened.return_value = True + self.mock_capture.return_value.grab.return_value = True + self.mock_capture.return_value.read.return_value = (True, img) + + assert brisque() == 0 + assert brisque(save=True) == 0 + + def test_good_image_from_file(self): + """ + Check if the test passes with a valid image from a file. + """ + + img_path = os.path.join(data_dir, "images/image_quality_good.jpg") + + assert brisque(file=img_path) == 0 + + def test_bad_image_from_file(self): + """ + Check if the test fails with a bad image. + """ + + # Set the mock + img_path = os.path.join(data_dir, "images/image_quality_bad.jpg") + + assert brisque(file=img_path) == 1 + + def test_invalid_image_from_file(self): + """ + Check if the test fails with a plain image. + """ + + # Set the mock + img_path = os.path.join(data_dir, "images/image_quality_plain.jpg") + + assert brisque(file=img_path) == 1 diff --git a/providers/base/tox.ini b/providers/base/tox.ini index 16f0557b92..b974b67f72 100644 --- a/providers/base/tox.ini +++ b/providers/base/tox.ini @@ -21,16 +21,18 @@ commands = deps = flake8 coverage == 5.5 - natsort == 4.0.3 - requests == 2.9.1 - urwid == 1.3.1 + distro == 1.0.1 Jinja2 == 2.8 + libsvm == 3.23.0.4 MarkupSafe == 0.23 - XlsxWriter == 0.7.3 + natsort == 4.0.3 + opencv_python == 4.4.0.42 + requests == 2.9.1 tqdm == 4.19.5 + urwid == 1.3.1 pyparsing == 2.0.3 - distro == 1.0.1 PyYAML == 3.11 + XlsxWriter == 0.7.3 setenv= # we do not care about the package version in tox # but it breaks some old python3.5 builds @@ -40,43 +42,49 @@ setenv= deps = flake8 coverage == 5.5 - natsort == 4.0.3 - requests == 2.18.4 - urwid == 2.0.1 + distro == 1.0.1 Jinja2 == 2.10 + libsvm == 3.23.0.4 MarkupSafe == 1.1.0 - XlsxWriter == 0.9.6 + natsort == 4.0.3 + opencv_python == 4.8.1.78 + requests == 2.18.4 tqdm == 4.19.5 + urwid == 2.0.1 pyparsing == 2.2.0 - distro == 1.0.1 PyYAML == 3.12 + XlsxWriter == 0.9.6 [testenv:py38] deps = flake8 coverage == 7.3.0 - natsort == 7.0.1 - requests == 2.22.0 - urwid == 2.0.1 + distro == 1.4.0 Jinja2 == 2.10.1 + libsvm == 3.23.0.4 MarkupSafe == 1.1.0 - XlsxWriter == 1.1.2 + natsort == 7.0.1 + opencv_python == 4.8.1.78 + requests == 2.22.0 tqdm == 4.30.0 + urwid == 2.0.1 pyparsing == 2.4.6 - distro == 1.4.0 PyYAML == 5.3.1 + XlsxWriter == 1.1.2 [testenv:py310] deps = flake8 coverage == 7.3.0 - natsort == 8.0.2 - requests == 2.25.1 - urwid == 2.1.2 + distro == 1.7.0 Jinja2 == 3.0.3 + libsvm == 3.23.0.4 MarkupSafe == 2.0.1 - XlsxWriter == 3.0.2 + natsort == 8.0.2 + opencv_python == 4.8.0.76 + requests == 2.25.1 tqdm == 4.57.0 + urwid == 2.1.2 pyparsing == 2.4.7 - distro == 1.7.0 PyYAML == 6.0.1 + XlsxWriter == 3.0.2 diff --git a/providers/base/units/camera/jobs.pxu b/providers/base/units/camera/jobs.pxu index 00a1826a9a..37be1dc695 100644 --- a/providers/base/units/camera/jobs.pxu +++ b/providers/base/units/camera/jobs.pxu @@ -77,14 +77,6 @@ flags: also-after-suspend _summary: Webcam still image capture test for {{ product_slug }} estimated_duration: 120.0 depends: camera/detect -requires: - {%- if __on_ubuntucore__ %} - executable.name == 'fswebcam' - {%- else %} - package.name == 'gir1.2-gst-plugins-base-1.0' - package.name == 'eog' - package.name == 'fswebcam' or package.name == 'gir1.2-gst-plugins-base-1.0' - {% endif -%} command: camera_test.py still -d /dev/{{ name }} _description: @@ -107,18 +99,32 @@ flags: also-after-suspend _summary: Webcam multiple resolution capture test for {{ product_slug }} estimated_duration: 1.2 depends: camera/detect -requires: - {%- if __on_ubuntucore__ %} - executable.name == 'fswebcam' - {%- else %} - package.name == 'fswebcam' or package.name == 'gir1.2-gst-plugins-base-1.0' - {% endif -%} command: camera_test.py resolutions -d /dev/{{ name }} _description: Takes multiple pictures based on the resolutions supported by the camera and validates their size and that they are of a valid format. +unit: template +template-resource: device +template-filter: device.category == 'CAPTURE' and device.name != '' +template-unit: job +plugin: shell +template-engine: jinja2 +category_id: com.canonical.plainbox::camera +id: camera/camera-quality_{{ name }} +flags: also-after-suspend +_summary: Webcam brisque score for {{ product_slug }} +estimated_duration: 20s +depends: camera/detect +command: + timeout 120 camera_quality_test.py -d {{ name }} || + (>&2 echo "Timeout computing score"; false) +_description: + Takes a picture and computes the quality based on a No-Reference image + quality assessment algorithm called BRISQUE. This test will timeout and fail + if the quality has not been compute within 120 seconds. + unit: template template-resource: device template-filter: device.category == 'MMAL' and device.name != '' diff --git a/providers/base/units/camera/packaging.pxu b/providers/base/units/camera/packaging.pxu index 0b90c61179..8a8b89d8e7 100644 --- a/providers/base/units/camera/packaging.pxu +++ b/providers/base/units/camera/packaging.pxu @@ -13,3 +13,14 @@ unit: packaging meta-data os-id: ubuntu Depends: python3-pyqrcode +# For camera/camera-quality_.* +unit: packaging meta-data +os-id: ubuntu +os-version-id: 22.04 +Depends: libsvm3 + +# For camera/camera-quality_.* +unit: packaging meta-data +os-id: ubuntu +os-version-id: 22.04 +Depends: python3-opencv diff --git a/providers/base/units/camera/test-plan.pxu b/providers/base/units/camera/test-plan.pxu index db75da7052..57c6a53ceb 100644 --- a/providers/base/units/camera/test-plan.pxu +++ b/providers/base/units/camera/test-plan.pxu @@ -36,6 +36,7 @@ _description: Camera tests (automated) include: camera/detect certification-status=blocker camera/multiple-resolution-images_.* certification-status=blocker + camera/camera-quality_.* certification-status=non-blocker bootstrap_include: device @@ -68,6 +69,7 @@ _description: Camera tests After Suspend (automated) include: after-suspend-camera/detect certification-status=blocker after-suspend-camera/multiple-resolution-images_.* certification-status=blocker + after-suspend-camera/camera-quality_* certification-status=non-blocker bootstrap_include: device