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277 lines
12 KiB
Python
277 lines
12 KiB
Python
"""
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Tool to load model and binarize a given image.
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"""
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import sys
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from glob import glob
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from os import environ, devnull
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from os.path import join
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from warnings import catch_warnings, simplefilter
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import numpy as np
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from PIL import Image
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import cv2
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environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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stderr = sys.stderr
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sys.stderr = open(devnull, 'w')
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import tensorflow as tf
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from tensorflow.keras.models import load_model
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from tensorflow.python.keras import backend as tensorflow_backend
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sys.stderr = stderr
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import logging
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def resize_image(img_in, input_height, input_width):
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return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
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class SbbBinarizer:
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def __init__(self, model_dir, logger=None):
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self.model_dir = model_dir
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self.log = logger if logger else logging.getLogger('SbbBinarizer')
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self.start_new_session()
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self.model_files = glob('%s/*.h5' % self.model_dir)
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if not self.model_files:
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raise ValueError(f"No models found in {self.model_dir}")
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self.models = []
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for model_file in self.model_files:
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self.models.append(self.load_model(model_file))
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def start_new_session(self):
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
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tensorflow_backend.set_session(self.session)
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def end_session(self):
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tensorflow_backend.clear_session()
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self.session.close()
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del self.session
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def load_model(self, model_name):
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model = load_model(model_name, compile=False)
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model_height = model.layers[len(model.layers)-1].output_shape[1]
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model_width = model.layers[len(model.layers)-1].output_shape[2]
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n_classes = model.layers[len(model.layers)-1].output_shape[3]
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return model, model_height, model_width, n_classes
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def predict(self, model_in, img, use_patches):
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tensorflow_backend.set_session(self.session)
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model, model_height, model_width, n_classes = model_in
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img_org_h = img.shape[0]
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img_org_w = img.shape[1]
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if img.shape[0] < model_height and img.shape[1] >= model_width:
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img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = 0
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img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
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elif img.shape[0] >= model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
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index_start_h = 0
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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elif img.shape[0] < model_height and img.shape[1] < model_width:
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img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
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index_start_h = int( abs( img.shape[0] - model_height) /2.)
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index_start_w = int( abs( img.shape[1] - model_width) /2.)
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img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
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else:
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index_start_h = 0
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index_start_w = 0
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img_padded = np.copy(img)
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img = np.copy(img_padded)
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if use_patches:
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margin = int(0.1 * model_width)
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width_mid = model_width - 2 * margin
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height_mid = model_height - 2 * margin
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img = img / float(255.0)
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img_h = img.shape[0]
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img_w = img.shape[1]
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prediction_true = np.zeros((img_h, img_w, 3))
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mask_true = np.zeros((img_h, img_w))
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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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if nyf > int(nyf):
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nyf = int(nyf) + 1
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else:
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nyf = int(nyf)
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for i in range(nxf):
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for j in range(nyf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + model_width
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if j == 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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elif j > 0:
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index_y_d = j * height_mid
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index_y_u = index_y_d + model_height
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if index_x_u > img_w:
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index_x_u = img_w
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index_x_d = img_w - model_width
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - model_height
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i == 0 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i == 0 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin, :] = seg_color
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elif i == nxf-1 and j != 0 and j != nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
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seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0, :] = seg_color
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elif i != 0 and i != nxf-1 and j == 0:
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seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
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elif i != 0 and i != nxf-1 and j == nyf-1:
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seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin, :] = seg_color
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else:
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seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
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seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
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mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
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prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin, :] = seg_color
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prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
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prediction_true = prediction_true.astype(np.uint8)
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else:
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img_h_page = img.shape[0]
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img_w_page = img.shape[1]
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img = img / float(255.0)
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img = resize_image(img, model_height, model_width)
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label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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prediction_true = resize_image(seg_color, img_h_page, img_w_page)
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prediction_true = prediction_true.astype(np.uint8)
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return prediction_true[:,:,0]
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def run(self, image=None, image_path=None, save=None, use_patches=False):
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if (image is not None and image_path is not None) or \
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(image is None and image_path is None):
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raise ValueError("Must pass either a opencv2 image or an image_path")
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if image_path is not None:
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image = cv2.imread(image_path)
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img_last = 0
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for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
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self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
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res = self.predict(model, image, use_patches)
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img_fin = np.zeros((res.shape[0], res.shape[1], 3))
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res[:, :][res[:, :] == 0] = 2
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res = res - 1
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res = res * 255
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img_fin[:, :, 0] = res
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img_fin[:, :, 1] = res
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img_fin[:, :, 2] = res
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img_fin = img_fin.astype(np.uint8)
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img_fin = (res[:, :] == 0) * 255
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img_last = img_last + img_fin
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kernel = np.ones((5, 5), np.uint8)
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img_last[:, :][img_last[:, :] > 0] = 255
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img_last = (img_last[:, :] == 0) * 255
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if save:
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cv2.imwrite(save, img_last)
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return img_last
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