sbb_binarization/sbb_binarize/sbb_binarize.py

"""
Tool to load model and binarize a given image.
"""

import sys
from glob import glob
from os import environ, devnull
from os.path import join
from warnings import catch_warnings, simplefilter

import numpy as np
from PIL import Image
import cv2
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
stderr = sys.stderr
sys.stderr = open(devnull, 'w')
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.python.keras import backend as tensorflow_backend
sys.stderr = stderr


import logging

def resize_image(img_in, input_height, input_width):
    return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

class SbbBinarizer:

    def __init__(self, model_dir, logger=None):
        self.model_dir = model_dir
        self.log = logger if logger else logging.getLogger('SbbBinarizer')

        self.start_new_session()

        self.model_files = glob('%s/*.h5' % self.model_dir)
        if not self.model_files:
            self.model_files = glob('%s/*/' % self.model_dir)
        if not self.model_files:
            raise ValueError(f"No models found in {self.model_dir}")
        
        self.models = []
        for model_file in self.model_files:
            self.models.append(self.load_model(model_file))

    def start_new_session(self):
        config = tf.compat.v1.ConfigProto()
        config.gpu_options.allow_growth = True

        self.session = tf.compat.v1.Session(config=config)  # tf.InteractiveSession()
        tensorflow_backend.set_session(self.session)

    def end_session(self):
        tensorflow_backend.clear_session()
        self.session.close()
        del self.session

    def load_model(self, model_name):
        model = load_model(model_name, compile=False)
        model_height = model.layers[len(model.layers)-1].output_shape[1]
        model_width = model.layers[len(model.layers)-1].output_shape[2]
        n_classes = model.layers[len(model.layers)-1].output_shape[3]
        return model, model_height, model_width, n_classes

    def predict(self, model_in, img):
        tensorflow_backend.set_session(self.session)
        model, model_height, model_width, n_classes = model_in
        
        img_org_h = img.shape[0]
        img_org_w = img.shape[1]
        
        if img.shape[0] < model_height and img.shape[1] >= model_width:
            img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
            
            index_start_h =  int( abs( img.shape[0] - model_height) /2.)
            index_start_w = 0
            
            img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
            
        elif img.shape[0] >= model_height and img.shape[1] < model_width:
            img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
            
            index_start_h =  0 
            index_start_w = int( abs( img.shape[1] - model_width) /2.)
            
            img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
            
            
        elif img.shape[0] < model_height and img.shape[1] < model_width:
            img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
            
            index_start_h =  int( abs( img.shape[0] - model_height) /2.)
            index_start_w = int( abs( img.shape[1] - model_width) /2.)
            
            img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
            
        else:
            index_start_h = 0
            index_start_w  = 0
            img_padded = np.copy(img)
            
            
        img = np.copy(img_padded)

        margin = int(0.1 * model_width)

        width_mid = model_width - 2 * margin
        height_mid = model_height - 2 * margin


        img = img / float(255.0)

        img_h = img.shape[0]
        img_w = img.shape[1]

        prediction_true = np.zeros((img_h, img_w, 3))
        mask_true = np.zeros((img_h, img_w))
        nxf = img_w / float(width_mid)
        nyf = img_h / float(height_mid)

        if nxf > int(nxf):
            nxf = int(nxf) + 1
        else:
            nxf = int(nxf)

        if nyf > int(nyf):
            nyf = int(nyf) + 1
        else:
            nyf = int(nyf)

        for i in range(nxf):
            for j in range(nyf):

                if i == 0:
                    index_x_d = i * width_mid
                    index_x_u = index_x_d + model_width
                elif i > 0:
                    index_x_d = i * width_mid
                    index_x_u = index_x_d + model_width

                if j == 0:
                    index_y_d = j * height_mid
                    index_y_u = index_y_d + model_height
                elif j > 0:
                    index_y_d = j * height_mid
                    index_y_u = index_y_d + model_height

                if index_x_u > img_w:
                    index_x_u = img_w
                    index_x_d = img_w - model_width
                if index_y_u > img_h:
                    index_y_u = img_h
                    index_y_d = img_h - model_height

                img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]

                label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
                                             verbose=0)

                seg = np.argmax(label_p_pred, axis=3)[0]

                seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)

                if i == 0 and j == 0:
                    seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
                    seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]

                    mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
                    prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color

                elif i == nxf-1 and j == nyf-1:
                    seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
                    seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]

                    mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
                    prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color

                elif i == 0 and j == nyf-1:
                    seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
                    seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]

                    mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
                    prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color

                elif i == nxf-1 and j == 0:
                    seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
                    seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]

                    mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
                    prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color

                elif i == 0 and j != 0 and j != nyf-1:
                    seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
                    seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]

                    mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
                    prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color

                elif i == nxf-1 and j != 0 and j != nyf-1:
                    seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
                    seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]

                    mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
                    prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color

                elif i != 0 and i != nxf-1 and j == 0:
                    seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
                    seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]

                    mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
                    prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color

                elif i != 0 and i != nxf-1 and j == nyf-1:
                    seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
                    seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]

                    mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
                    prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color

                else:
                    seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
                    seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]

                    mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
                    prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
        
        
        
        prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
        prediction_true = prediction_true.astype(np.uint8)

        return prediction_true[:,:,0]

    def run(self, image=None, image_path=None, save=None):
        if (image is not None and image_path is not None) or \
               (image is None and image_path is None):
            raise ValueError("Must pass either a opencv2 image or an image_path")
        if image_path is not None:
            image = cv2.imread(image_path)
        img_last = 0
        for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
            self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))

            res = self.predict(model, image)

            img_fin = np.zeros((res.shape[0], res.shape[1], 3))
            res[:, :][res[:, :] == 0] = 2
            res = res - 1
            res = res * 255
            img_fin[:, :, 0] = res
            img_fin[:, :, 1] = res
            img_fin[:, :, 2] = res

            img_fin = img_fin.astype(np.uint8)
            img_fin = (res[:, :] == 0) * 255
            img_last = img_last + img_fin

        img_last[:, :][img_last[:, :] > 0] = 255
        img_last = (img_last[:, :] == 0) * 255
        if save:
            cv2.imwrite(save, img_last.astype(np.uint8), [
                cv2.IMWRITE_PNG_BILEVEL, 1,
                cv2.IMWRITE_JPEG_QUALITY, 100
            ])
        return img_last