我们从Python开源项目中，提取了以下12个代码示例，用于说明如何使用skimage.exposure.equalize_hist()。
def rgb2illumination_invariant(img, alpha, hist_eq=False): """ this is an implementation of the illuminant-invariant color space published by Maddern2014 http://www.robots.ox.ac.uk/~mobile/Papers/2014ICRA_maddern.pdf :param img: :param alpha: camera paramete :return: """ ii_img = 0.5 + np.log(img[:, :, 1] + 1e-8) - \ alpha * np.log(img[:, :, 2] + 1e-8) - \ (1 - alpha) * np.log(img[:, :, 0] + 1e-8) # ii_img = exposure.rescale_intensity(ii_img, out_range=(0, 1)) if hist_eq: ii_img = exposure.equalize_hist(ii_img) print np.max(ii_img) print np.min(ii_img) return ii_img
def visualize_derivatives(image): ''' Plot gradient on left and Laplacian on right. Only tested on 2D 1-channel float imags ''' dx1,dy1 = np.gradient(image) gradient = dx1 + 1j*dy1 a1 = np.abs(gradient) plt.figure(None,(12,6)) plt.subplot(121) a1 = mean_center(blur(exposure.equalize_hist(unitize(a1)),1)) plt.imshow(a1, origin='lower',interpolation='nearest',cmap='gray',extent=(0,64,)*2) plt.title('Gradient Magnitude') plt.subplot(122) laplacian = scipy.ndimage.filters.laplace(image) lhist = mean_center( blur(exposure.equalize_hist(unitize(laplacian)),1)) plt.imshow(lhist, origin='lower',interpolation='nearest',cmap='gray',extent=(0,64,)*2) plt.title('Laplacian') return gradient, laplacian
def equalize(image): from skimage import exposure return exposure.equalize_hist(image)
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 _equalizeHistogram(img): ''' histogram equalisation not bounded to int() or an image depth of 8 bit works also with negative numbers ''' # to float if int: intType = None if 'f' not in img.dtype.str: TO_FLOAT_TYPES = {np.dtype('uint8'): np.float16, np.dtype('uint16'): np.float32, np.dtype('uint32'): np.float64, np.dtype('uint64'): np.float64} intType = img.dtype img = img.astype(TO_FLOAT_TYPES[intType], copy=False) # get image deph DEPTH_TO_NBINS = {np.dtype('float16'): 256, # uint8 np.dtype('float32'): 32768, # uint16 np.dtype('float64'): 2147483648} # uint32 nBins = DEPTH_TO_NBINS[img.dtype] # scale to -1 to 1 due to skikit-image restrictions mn, mx = np.amin(img), np.amax(img) if abs(mn) > abs(mx): mx = mn img /= mx img = exposure.equalize_hist(img, nbins=nBins) img *= mx if intType: img = img.astype(intType) return img
def process(self, im): return exposure.equalize_hist(im)
def draw_center_for_check(dcm_path, id, slicelocalization,PixelSpacing,sax, point, points): debug_folder = os.path.join('E:', 'calc', 'center_find') if not os.path.isdir(debug_folder): os.mkdir(debug_folder) ds = pydicom.read_file(dcm_path) img = convert_to_grayscale_with_increase_brightness_fast(ds.pixel_array, 1) raw=int(round(point[0], 0)) #F_col=float(col) col=int(round(point[1], 0)) #F_raw=float(raw) row_spacing =PixelSpacing[0] row_spacing = float(row_spacing) row_spacing_size=(32.0*1.4)/row_spacing col_spacing = PixelSpacing[1] col_spacing = float(col_spacing) col_spacing_size=(32.0*1.4)/col_spacing L_raw=raw-int(row_spacing_size) R_raw=raw+int(row_spacing_size) L_col=col-int(col_spacing_size) R_col=col+int(col_spacing_size) crop_img = img[L_raw:R_raw, L_col:R_col] img_shape = (64, 64) crop_img = imresize(crop_img, img_shape) crop_img=exposure.equalize_hist(crop_img) crop_img=crop_img*255 crop_img=crop_img.astype(np.uint8) #cv2.circle(img, (int(round(point[1], 0)), int(round(point[0], 0))), 5, 255, 3) #cv2.circle(img, 15, 25, 5, 255, 3) #img = cv2.line(img, (points[1], points[0]), (points[3], points[2]), 127, thickness=2) #img = cv2.line(img, (points[5], points[4]), (points[7], points[6]), 127, thickness=2) #img = cv2.line(img, (12, 13), (112, 223), 127, thickness=2) #img = cv2.line(img, (12,115), (112,256), 127, thickness=2) #show_image(img) cv2.imwrite(os.path.join(debug_folder, str(id) + '_' + sax + '_'+ slicelocalization+'.jpg'), crop_img)
def crop_resize_check(dcm_path, id, slicelocalization,PixelSpacing,sax, point, imagename): dcm_path = dcm_path.replace('\\', '/') debug_folder = os.path.join('E:', 'calc', 'checkEDES') if not os.path.isdir(debug_folder): os.mkdir(debug_folder) ds = dicom.read_file(dcm_path) img = convert_to_grayscale_with_increase_brightness_fast(ds.pixel_array, 1) raw=int(round(point[0], 0)) #F_col=float(col) col=int(round(point[1], 0)) #F_raw=float(raw) row_spacing =PixelSpacing[0] row_spacing = float(row_spacing) row_spacing_size=(32.0*1.4)/row_spacing col_spacing = PixelSpacing[1] col_spacing = float(col_spacing) col_spacing_size=(32.0*1.4)/col_spacing L_raw=raw-int(row_spacing_size) R_raw=raw+int(row_spacing_size) L_col=col-int(col_spacing_size) R_col=col+int(col_spacing_size) crop_img = img[L_raw:R_raw, L_col:R_col] #img_shape = (64, 64) crop_img = imresize(crop_img, img_shape) crop_img=exposure.equalize_hist(crop_img) crop_img=crop_img*255 crop_img=crop_img.astype(np.uint8) #cv2.circle(img, (int(round(point[1], 0)), int(round(point[0], 0))), 5, 255, 3) #cv2.circle(img, 15, 25, 5, 255, 3) #img = cv2.line(img, (points[1], points[0]), (points[3], points[2]), 127, thickness=2) #img = cv2.line(img, (points[5], points[4]), (points[7], points[6]), 127, thickness=2) #img = cv2.line(img, (12, 13), (112, 223), 127, thickness=2) #img = cv2.line(img, (12,115), (112,256), 127, thickness=2) #show_image(img) cv2.imwrite(os.path.join(debug_folder, str(id) + '_' + sax + '_'+ imagename+'_'+str(slicelocalization)+'.jpg'), crop_img) return crop_img
def visualize_derivatives(image): laplacian = scipy.ndimage.filters.laplace(image) lhist = mean_center( blur(exposure.equalize_hist(unitize(laplacian)),1)) plt.imshow(lhist, origin='lower',interpolation='nearest',cmap='gray',extent=(0,64,)*2) plt.title('Laplacian') return gradient, laplacian
def plot_box(box, title=None, path=None, format=None, scale="log", interval="pts", cmap="viridis"): """ This function ... :param box: :param title: :param path: :param format: :param scale: :param interval: :param cmap: :return: """ # Other new colormaps: plasma, magma, inferno # Normalization if scale == "log": norm = ImageNormalize(stretch=LogStretch()) elif scale == "sqrt": norm = ImageNormalize(stretch=SqrtStretch()) #elif scale == "skimage": norm = exposure.equalize_hist else: raise ValueError("Invalid option for 'scale'") if interval == "zscale": vmin, vmax = ZScaleInterval().get_limits(box) elif interval == "pts": # Determine the maximum value in the box and the mimimum value for plotting vmin = max(np.nanmin(box), 0.) vmax = 0.5 * (np.nanmax(box) + vmin) elif isinstance(interval, tuple): vmin = interval[0] vmax = interval[1] else: raise ValueError("Invalid option for 'interval'") #if scale == "skimage": # vmin = 0.0 # vmax = 1.0 # Make the plot plt.figure(figsize=(7,7)) plt.imshow(box, origin="lower", interpolation="nearest", vmin=vmin, vmax=vmax, norm=norm, cmap=cmap) plt.xlim(0, box.shape[1]-1) plt.ylim(0, box.shape[0]-1) if title is not None: plt.title(title) if path is None: plt.show() else: plt.savefig(path, format=format) plt.close() # -----------------------------------------------------------------
def read_fold(fold_csv_prefix,fold_image_prefix,fold_names): width = 256 height = 256 i=0 for fold in fold_names: print("Reading fold: %s" % fold) df = pd.read_csv(fold_csv_prefix+fold+'.csv') inputimages = [] genders = [] ages = [] for index, row in df.iterrows(): yaw_angle = row['fiducial_yaw_angle'] gender = row['gender'] age = row['age'] if ((gender!='u') and (gender!='Nan') and (age!='None') and (gender!=' ') and (age!=' ') and (yaw_angle >= -45) and (yaw_angle <= 45)): folder_name = row['user_id'] image_name = row['original_image'] face_id = row['face_id'] age_tuple = make_tuple(age) age_id = get_age_range_id(age_tuple) image_path = fold_image_prefix+folder_name+'/landmark_aligned_face.'+str(face_id)+'.'+image_name image = Image.open(image_path) #Resize image image = image.resize((width, height), PIL.Image.ANTIALIAS) image_arr = np.array(image) #image_arr = exposure.equalize_hist(image_arr) if(gender == 'm'): g=0 else: g=1 inputimages.append(image_arr) genders.append(g) ages.append(age_id) print('Done: {0}/{1} folds'.format(i, len(fold_names))) i=i+1 print ('Fold Name: %s' % fold) print ('Images: %i, Gender: %i, Ages: %i' % (len(inputimages), len(genders), len(ages))) print ('') currDict = {'fold_name': fold, 'images': inputimages, 'genders': genders, 'ages': ages} save_pickle(currDict,fold, '/home/narita/Documents/pythonworkspace/data-science-practicum/gender-age-classification/gender_neutral_data/')
