inference script is added
vahidrezanezhad
2 weeks ago
parent
38db3e9289
commit
8d1050ec30
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#! /usr/bin/env python3
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__version__= '1.0'
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import argparse
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import sys
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import os
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import numpy as np
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import warnings
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import xml.etree.ElementTree as et
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import pandas as pd
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from tqdm import tqdm
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import csv
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import cv2
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import seaborn as sns
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import matplotlib.pyplot as plt
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from tensorflow.keras.models import load_model
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import tensorflow as tf
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from tensorflow.keras import backend as K
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from tensorflow.keras import layers
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import tensorflow.keras.losses
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from tensorflow.keras.layers import *
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import click
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import json
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from tensorflow.python.keras import backend as tensorflow_backend
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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__doc__=\
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"""
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Tool to load model and predict for given image.
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"""
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projection_dim = 64
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patch_size = 1
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num_patches =28*28
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class Patches(layers.Layer):
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def __init__(self, **kwargs):
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super(Patches, self).__init__()
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self.patch_size = patch_size
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def call(self, images):
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print(tf.shape(images)[1],'images')
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print(self.patch_size,'self.patch_size')
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batch_size = tf.shape(images)[0]
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patches = tf.image.extract_patches(
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images=images,
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sizes=[1, self.patch_size, self.patch_size, 1],
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strides=[1, self.patch_size, self.patch_size, 1],
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rates=[1, 1, 1, 1],
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padding="VALID",
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)
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patch_dims = patches.shape[-1]
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print(patches.shape,patch_dims,'patch_dims')
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patches = tf.reshape(patches, [batch_size, -1, patch_dims])
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return patches
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def get_config(self):
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config = super().get_config().copy()
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config.update({
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'patch_size': self.patch_size,
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})
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return config
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class PatchEncoder(layers.Layer):
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def __init__(self, **kwargs):
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super(PatchEncoder, self).__init__()
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self.num_patches = num_patches
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self.projection = layers.Dense(units=projection_dim)
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self.position_embedding = layers.Embedding(
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input_dim=num_patches, output_dim=projection_dim
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)
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def call(self, patch):
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positions = tf.range(start=0, limit=self.num_patches, delta=1)
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encoded = self.projection(patch) + self.position_embedding(positions)
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return encoded
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def get_config(self):
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config = super().get_config().copy()
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config.update({
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'num_patches': self.num_patches,
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'projection': self.projection,
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'position_embedding': self.position_embedding,
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})
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return config
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class sbb_predict:
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def __init__(self,image, model, task, config_params_model, patches='false',save='false', ground_truth=None,weights_dir=None ):
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self.image=image
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self.patches=patches
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self.save=save
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self.model_dir=model
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self.ground_truth=ground_truth
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self.weights_dir=weights_dir
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self.task=task
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self.config_params_model=config_params_model
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def resize_image(self,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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def color_images(self,seg):
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ann_u=range(self.n_classes)
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if len(np.shape(seg))==3:
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seg=seg[:,:,0]
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seg_img=np.zeros((np.shape(seg)[0],np.shape(seg)[1],3)).astype(np.uint8)
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colors=sns.color_palette("hls", self.n_classes)
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for c in ann_u:
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c=int(c)
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segl=(seg==c)
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seg_img[:,:,0][seg==c]=c
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seg_img[:,:,1][seg==c]=c
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seg_img[:,:,2][seg==c]=c
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return seg_img
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def otsu_copy_binary(self,img):
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img_r=np.zeros((img.shape[0],img.shape[1],3))
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img1=img[:,:,0]
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#print(img.min())
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#print(img[:,:,0].min())
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#blur = cv2.GaussianBlur(img,(5,5))
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#ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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img_r[:,:,0]=threshold1
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img_r[:,:,1]=threshold1
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img_r[:,:,2]=threshold1
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#img_r=img_r/float(np.max(img_r))*255
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return img_r
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def otsu_copy(self,img):
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img_r=np.zeros((img.shape[0],img.shape[1],3))
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#img1=img[:,:,0]
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#print(img.min())
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#print(img[:,:,0].min())
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#blur = cv2.GaussianBlur(img,(5,5))
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#ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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_, threshold1 = cv2.threshold(img[:,:,0], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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_, threshold2 = cv2.threshold(img[:,:,1], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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_, threshold3 = cv2.threshold(img[:,:,2], 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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img_r[:,:,0]=threshold1
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img_r[:,:,1]=threshold2
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img_r[:,:,2]=threshold3
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###img_r=img_r/float(np.max(img_r))*255
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return img_r
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def soft_dice_loss(self,y_true, y_pred, epsilon=1e-6):
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axes = tuple(range(1, len(y_pred.shape)-1))
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numerator = 2. * K.sum(y_pred * y_true, axes)
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denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
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return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
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def weighted_categorical_crossentropy(self,weights=None):
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def loss(y_true, y_pred):
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labels_floats = tf.cast(y_true, tf.float32)
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per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
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if weights is not None:
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weight_mask = tf.maximum(tf.reduce_max(tf.constant(
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np.array(weights, dtype=np.float32)[None, None, None])
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* labels_floats, axis=-1), 1.0)
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per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
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return tf.reduce_mean(per_pixel_loss)
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return self.loss
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def IoU(self,Yi,y_predi):
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## mean Intersection over Union
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## Mean IoU = TP/(FN + TP + FP)
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IoUs = []
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Nclass = np.unique(Yi)
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for c in Nclass:
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TP = np.sum( (Yi == c)&(y_predi==c) )
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FP = np.sum( (Yi != c)&(y_predi==c) )
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FN = np.sum( (Yi == c)&(y_predi != c))
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IoU = TP/float(TP + FP + FN)
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if self.n_classes>2:
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print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c,TP,FP,FN,IoU))
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IoUs.append(IoU)
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if self.n_classes>2:
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mIoU = np.mean(IoUs)
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print("_________________")
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print("Mean IoU: {:4.3f}".format(mIoU))
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return mIoU
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elif self.n_classes==2:
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mIoU = IoUs[1]
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print("_________________")
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print("IoU: {:4.3f}".format(mIoU))
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return mIoU
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def start_new_session_and_model(self):
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config = tf.compat.v1.ConfigProto()
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config.gpu_options.allow_growth = True
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session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
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tensorflow_backend.set_session(session)
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#tensorflow.keras.layers.custom_layer = PatchEncoder
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#tensorflow.keras.layers.custom_layer = Patches
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self.model = load_model(self.model_dir , compile=False,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
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#config = tf.ConfigProto()
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#config.gpu_options.allow_growth=True
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#self.session = tf.InteractiveSession()
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#keras.losses.custom_loss = self.weighted_categorical_crossentropy
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#self.model = load_model(self.model_dir , compile=False)
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##if self.weights_dir!=None:
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##self.model.load_weights(self.weights_dir)
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if self.task != 'classification':
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self.img_height=self.model.layers[len(self.model.layers)-1].output_shape[1]
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self.img_width=self.model.layers[len(self.model.layers)-1].output_shape[2]
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self.n_classes=self.model.layers[len(self.model.layers)-1].output_shape[3]
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def visualize_model_output(self, prediction, img, task):
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if task == "binarization":
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prediction = prediction * -1
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prediction = prediction + 1
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added_image = prediction * 255
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else:
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unique_classes = np.unique(prediction[:,:,0])
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rgb_colors = {'0' : [255, 255, 255],
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'1' : [255, 0, 0],
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'2' : [255, 125, 0],
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'3' : [255, 0, 125],
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'4' : [125, 125, 125],
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'5' : [125, 125, 0],
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'6' : [0, 125, 255],
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'7' : [0, 125, 0],
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'8' : [125, 125, 125],
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'9' : [0, 125, 255],
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'10' : [125, 0, 125],
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'11' : [0, 255, 0],
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'12' : [0, 0, 255],
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'13' : [0, 255, 255],
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'14' : [255, 125, 125],
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'15' : [255, 0, 255]}
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output = np.zeros(prediction.shape)
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for unq_class in unique_classes:
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rgb_class_unique = rgb_colors[str(int(unq_class))]
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output[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0]
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output[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1]
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output[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2]
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img = self.resize_image(img, output.shape[0], output.shape[1])
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output = output.astype(np.int32)
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img = img.astype(np.int32)
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added_image = cv2.addWeighted(img,0.5,output,0.1,0)
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return added_image
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def predict(self):
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self.start_new_session_and_model()
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if self.task == 'classification':
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classes_names = self.config_params_model['classification_classes_name']
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img_1ch = img=cv2.imread(self.image, 0)
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img_1ch = img_1ch / 255.0
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img_1ch = cv2.resize(img_1ch, (self.config_params_model['input_height'], self.config_params_model['input_width']), interpolation=cv2.INTER_NEAREST)
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img_in = np.zeros((1, img_1ch.shape[0], img_1ch.shape[1], 3))
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img_in[0, :, :, 0] = img_1ch[:, :]
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img_in[0, :, :, 1] = img_1ch[:, :]
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img_in[0, :, :, 2] = img_1ch[:, :]
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label_p_pred = self.model.predict(img_in, verbose=0)
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index_class = np.argmax(label_p_pred[0])
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print("Predicted Class: {}".format(classes_names[str(int(index_class))]))
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else:
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if self.patches:
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#def textline_contours(img,input_width,input_height,n_classes,model):
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img=cv2.imread(self.image)
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self.img_org = np.copy(img)
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if img.shape[0] < self.img_height:
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img = cv2.resize(img, (img.shape[1], self.img_width), interpolation=cv2.INTER_NEAREST)
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if img.shape[1] < self.img_width:
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img = cv2.resize(img, (self.img_height, img.shape[0]), interpolation=cv2.INTER_NEAREST)
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margin = int(0 * self.img_width)
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width_mid = self.img_width - 2 * margin
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height_mid = self.img_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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nxf = img_w / float(width_mid)
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nyf = img_h / float(height_mid)
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nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
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nyf = int(nyf) + 1 if nyf > int(nyf) else 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 + self.img_width
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else:
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index_x_d = i * width_mid
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index_x_u = index_x_d + self.img_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 + self.img_height
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else:
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index_y_d = j * height_mid
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index_y_u = index_y_d + self.img_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 - self.img_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 - self.img_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 = self.model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
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verbose=0)
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if self.task == 'enhancement':
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seg = label_p_pred[0, :, :, :]
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seg = seg * 255
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elif self.task == 'segmentation' or self.task == 'binarization':
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i == 0 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg
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elif i == 0 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
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elif i == 0 and j != 0 and j != nyf - 1:
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seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg
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elif i == nxf - 1 and j != 0 and j != nyf - 1:
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg
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elif i != 0 and i != nxf - 1 and j == 0:
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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elif i != 0 and i != nxf - 1 and j == nyf - 1:
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg
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else:
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
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prediction_true = prediction_true.astype(int)
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prediction_true = cv2.resize(prediction_true, (self.img_org.shape[1], self.img_org.shape[0]), interpolation=cv2.INTER_NEAREST)
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return prediction_true
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else:
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img=cv2.imread(self.image)
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self.img_org = np.copy(img)
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width=self.img_width
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height=self.img_height
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img=img/255.0
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img=self.resize_image(img,self.img_height,self.img_width)
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||||
|
||||
|
||||
label_p_pred=self.model.predict(
|
||||
img.reshape(1,img.shape[0],img.shape[1],img.shape[2]))
|
||||
|
||||
if self.task == 'enhancement':
|
||||
seg = label_p_pred[0, :, :, :]
|
||||
seg = seg * 255
|
||||
elif self.task == 'segmentation' or self.task == 'binarization':
|
||||
seg = np.argmax(label_p_pred, axis=3)[0]
|
||||
seg = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
|
||||
|
||||
prediction_true = seg.astype(int)
|
||||
|
||||
prediction_true = cv2.resize(prediction_true, (self.img_org.shape[1], self.img_org.shape[0]), interpolation=cv2.INTER_NEAREST)
|
||||
return prediction_true
|
||||
|
||||
|
||||
|
||||
def run(self):
|
||||
res=self.predict()
|
||||
if self.task == 'classification':
|
||||
pass
|
||||
else:
|
||||
img_seg_overlayed = self.visualize_model_output(res, self.img_org, self.task)
|
||||
cv2.imwrite('./test.png',img_seg_overlayed)
|
||||
##if self.save!=None:
|
||||
##img=np.repeat(res[:, :, np.newaxis]*255, 3, axis=2)
|
||||
##cv2.imwrite(self.save,img)
|
||||
|
||||
###if self.ground_truth!=None:
|
||||
###gt_img=cv2.imread(self.ground_truth)
|
||||
###self.IoU(gt_img[:,:,0],res)
|
||||
##plt.imshow(res)
|
||||
##plt.show()
|
||||
|
||||
@click.command()
|
||||
@click.option(
|
||||
"--image",
|
||||
"-i",
|
||||
help="image filename",
|
||||
type=click.Path(exists=True, dir_okay=False),
|
||||
)
|
||||
@click.option(
|
||||
"--patches/--no-patches",
|
||||
"-p/-nop",
|
||||
is_flag=True,
|
||||
help="if this parameter set to true, this tool will try to do inference in patches.",
|
||||
)
|
||||
@click.option(
|
||||
"--save",
|
||||
"-s",
|
||||
help="save prediction as a png file in current folder.",
|
||||
)
|
||||
@click.option(
|
||||
"--model",
|
||||
"-m",
|
||||
help="directory of models",
|
||||
type=click.Path(exists=True, file_okay=False),
|
||||
required=True,
|
||||
)
|
||||
@click.option(
|
||||
"--ground_truth/--no-ground_truth",
|
||||
"-gt/-nogt",
|
||||
is_flag=True,
|
||||
help="ground truth directory if you want to see the iou of prediction.",
|
||||
)
|
||||
@click.option(
|
||||
"--model_weights/--no-model_weights",
|
||||
"-mw/-nomw",
|
||||
is_flag=True,
|
||||
help="previous model weights which are saved.",
|
||||
)
|
||||
def main(image, model, patches, save, ground_truth, model_weights):
|
||||
|
||||
with open(os.path.join(model,'config.json')) as f:
|
||||
config_params_model = json.load(f)
|
||||
task = 'classification'
|
||||
x=sbb_predict(image, model, task, config_params_model, patches, save, ground_truth, model_weights)
|
||||
x.run()
|
||||
|
||||
if __name__=="__main__":
|
||||
main()
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue