def value(self): global request_count if self._value is None: cache_path = os.path.join("quandl_cache", self.quandl_name + ".csv") if os.path.exists(cache_path): self._value = self._fix_missing(DataFrame.from_csv(cache_path)) elif os.path.exists(cache_path + ".fail"): raise NoDataError("Previously failed") elif self.quandl_disable: raise NoDataError("Quandl Disabled due to rate limit") else: self._retrieve_from_quandl(cache_path) return self._value
kernel = gabor_kernel(frequency, theta=theta, sigma_x=sigma, sigma_y=sigma) kernels.append(kernel) # imshow(np.real(kernel)) # show() positives = [] negatives = [] for s in samples: input_path = os.path.join(folder, s, 'input.tif') expected_path = os.path.join(folder, s, 'expected.tif') data_path = os.path.join(folder, s, 'data.csv') input_image = Image.open(input_path) expected_image = Image.open(expected_path) data = DataFrame.from_csv(data_path) a = np.asarray(input_image) responses = np.empty((len(kernels), a.shape[0], a.shape[1])) for i, k in enumerate(kernels): response = responses[i, :, :] = np.hypot(convolve(a, np.real(k)), convolve(a, np.imag(k))) #imshow(response) scores = [] for i in range(9): j = (i + 9) % 18 x_angle = angles[i] y_angle = angles[j] c = cos(x_angle)