我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用skimage.exposure.equalize_adapthist()。
def get_data(image_id, a_size, m_size, p_size, sf): rgb_data = get_rgb_data(image_id) rgb_data = cv2.resize(rgb_data, (p_size*sf, p_size*sf), interpolation=cv2.INTER_LANCZOS4) # rgb_data = rgb_data.astype(np.float) / 2500. # print(np.max(rgb_data), np.mean(rgb_data)) # rgb_data[:, :, 0] = exposure.equalize_adapthist(rgb_data[:, :, 0], clip_limit=0.04) # rgb_data[:, :, 1] = exposure.equalize_adapthist(rgb_data[:, :, 1], clip_limit=0.04) # rgb_data[:, :, 2] = exposure.equalize_adapthist(rgb_data[:, :, 2], clip_limit=0.04) A_data = get_spectral_data(image_id, a_size*sf, a_size*sf, bands=['A']) M_data = get_spectral_data(image_id, m_size*sf, m_size*sf, bands=['M']) P_data = get_spectral_data(image_id, p_size*sf, p_size*sf, bands=['P']) # lab_data = cv2.cvtColor(rgb_data, cv2.COLOR_BGR2LAB) P_data = np.concatenate([rgb_data, P_data], axis=2) return A_data, M_data, P_data
def clahe_normalization(img, kernel_size=3, nbins=1024, clip_limit=0.3): '''Contrast Limited Adaptive Histogram Equalization (CLAHE).''' return equalize_adapthist(img, kernel_size, nbins, clip_limit)
def scaling(image, method="stretching"): """ Change the image dynamic. Parameters ---------- image: Image the image to be transformed. method: str, default 'stretching' the normalization method: 'stretching', 'equalization' or 'adaptive'. Returns ------- normalize_image: Image the normalized image. """ # Contrast stretching if method == "stretching": p2, p98 = np.percentile(image.data, (2, 98)) norm_data = exposure.rescale_intensity(image.data, in_range=(p2, p98)) # Equalization elif method == "equalization": norm_data = exposure.equalize_hist(image.data) # Adaptive Equalization elif method == "adaptive": norm_data = exposure.equalize_adapthist(image.data, clip_limit=0.03) # Unknown method else: raise ValueError("Unknown normalization '{0}'.".format(method)) normalize_image = pisap.Image(data=norm_data) return normalize_image
def normalize_histo(image, gamma=1.0): """ Perform histogram normalization on image. :param numpy array image: Numpy array with range [0,255] and dtype 'uint8'. :param float gamma: Factor for gamma adjustment. :return: Normalized image :rtype: numpy array with range [0,255] and dtype 'uint8' """ image = ske.equalize_adapthist(image) image = ske.adjust_gamma(image, gamma=gamma) return floatimg2uint8(image)
def equalize_adapthist(img, val=None): return exposure.equalize_adapthist(img)
