>> from keras.preprocessing.image import img_to_array >>> image = img_to_array (image) By now, we have the number representation of our image. Input (3) Output Execution Info Log Comments (0) Best Submission. We'll also see how we can work with MobileNets in code using TensorFlow's Keras API. keras. Successful. interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. In Keras, we have ImageDataGenerator API, which generates the images in batches with real-time data augmentation. Inception v3 in TensorFlow. This is Part 2 of a MNIST digit classification notebook. If we have a model that takes in an image as its input, and outputs class scores, i.e. from tensorflow import keras. Input pipeline using Tensorflow will create tensors as an input to the model. from keras_preprocessing.image import ImageDataGenerator from keras.layers import … import matplotlib.pyplot as plt import seaborn as sns import keras from keras.models import Sequential from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam from sklearn.metrics import classification_report,confusion_matrix import tensorflow as tf import cv2 import … json, xml, binary) and how your model is expecting it (e.g. Explaining Keras image classifier predictions with Grad-CAM¶. [ ] Setup [ ] [ ] import tensorflow as tf. These are the pixel values of the image stored in a 2D matrix. COCO animals dataset and pre-processing images. Looking to image preprocessing example in Keras, you often see image is scaled down by factor 255 before feeding to the model. Keras center and random crop support for ImageDataGenerator. It first resizes image preserving aspect ratio and then performs crop. Numpy will be used for creating a new dimension and Keras for preprocessing and importing the resnet50 pre-trained model. tf.keras.preprocessing.image_dataset_from_directory(. Transfer Learning in Keras (Image Recognition) Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Credit to Robin Smits and lafoss. from keras.layers import Conv2D, MaxPooling2D. Input PIL Image instance. We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation. image package. These input processing pipelines can be used as independent preprocessing code in non-Keras workflows, combined directly with Keras models, and exported as part of a Keras SavedModel. Here's a quick example: let's say you have 10 folders, each containing 10,000 images from a different category, and you want to train a classifier that maps an image to its category. Keras Keras2.0. Brightness_range Keras is an argument in ImageDataGenerator class of keras. Using the IMAGE_PATH we load the image and then construct the payload to the request. ページによって引数の説明があったりなかったりするので. Keras dataset preprocessing utilities, located at tf.keras.preprocessing, help you go from raw data on disk to a tf.data.Dataset object that can be used to train a model. img_width, img_height = 320, 240. train_data_dir = 'data/train' VGG16 in TensorFlow. from tensorflow import keras. It is written in Python, though - so I adapted the code to R. Keras Preprocessing Layers Keras has preprocessing layers so that you can preprocess your data as part of a model. format) 10 print (img. # the data for training and the remaining 25% for testing. def preprocess (image_path): img = image.load_img (image_path, target_size= (299, 299)) x = image.img_to_array (img) x = np.expand_dims (x, axis=0) x = preprocess_input (x) return x. and I'm getting the following error: AttributeError: module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory' tensorflow version: 2.2.0 keras version: 2.3.0-tf. In my example train_cropped.py code, I used ImageDataGenerator.flow_from_directory() to resize all input images to (256, 256) and then use my own crop_generator to generate random (224, 224) crops from the resized images. The larger size is then cropped in order to produce a square image. from keras.applications.vgg16 import VGG16 model = VGG16(weights='imagenet', include_top=False) If you want to stick to the original preprocessing steps, you can find pre-trained weights that is trained with the original preprocessing steps instead of using the weights that comes with Keras. smart_resize: If True, the resizing function used will be tf.keras.preprocessing.image.smart_resize, which preserves the aspect ratio of the original image by using a mixture of resizing and cropping. Data preprocessing and data augmentation module of the Keras deep learning library … If we have a model that takes in an image as its input, and outputs class scores, i.e. Wow Classic Raid Spreadsheet, Computer Input And Output Devices Notes, Please Do Not Touch Magazine, Spraying Water On Burgers, The Impact Of Digital Transformation In The Finance Industry, Calgary Vinyl Records, What Did John Wesley Shipp Play In, Redbridge 11 Plus Common Catchment Area, " /> >> from keras.preprocessing.image import img_to_array >>> image = img_to_array (image) By now, we have the number representation of our image. Input (3) Output Execution Info Log Comments (0) Best Submission. We'll also see how we can work with MobileNets in code using TensorFlow's Keras API. keras. Successful. interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. In Keras, we have ImageDataGenerator API, which generates the images in batches with real-time data augmentation. Inception v3 in TensorFlow. This is Part 2 of a MNIST digit classification notebook. If we have a model that takes in an image as its input, and outputs class scores, i.e. from tensorflow import keras. Input pipeline using Tensorflow will create tensors as an input to the model. from keras_preprocessing.image import ImageDataGenerator from keras.layers import … import matplotlib.pyplot as plt import seaborn as sns import keras from keras.models import Sequential from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam from sklearn.metrics import classification_report,confusion_matrix import tensorflow as tf import cv2 import … json, xml, binary) and how your model is expecting it (e.g. Explaining Keras image classifier predictions with Grad-CAM¶. [ ] Setup [ ] [ ] import tensorflow as tf. These are the pixel values of the image stored in a 2D matrix. COCO animals dataset and pre-processing images. Looking to image preprocessing example in Keras, you often see image is scaled down by factor 255 before feeding to the model. Keras center and random crop support for ImageDataGenerator. It first resizes image preserving aspect ratio and then performs crop. Numpy will be used for creating a new dimension and Keras for preprocessing and importing the resnet50 pre-trained model. tf.keras.preprocessing.image_dataset_from_directory(. Transfer Learning in Keras (Image Recognition) Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Credit to Robin Smits and lafoss. from keras.layers import Conv2D, MaxPooling2D. Input PIL Image instance. We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation. image package. These input processing pipelines can be used as independent preprocessing code in non-Keras workflows, combined directly with Keras models, and exported as part of a Keras SavedModel. Here's a quick example: let's say you have 10 folders, each containing 10,000 images from a different category, and you want to train a classifier that maps an image to its category. Keras Keras2.0. Brightness_range Keras is an argument in ImageDataGenerator class of keras. Using the IMAGE_PATH we load the image and then construct the payload to the request. ページによって引数の説明があったりなかったりするので. Keras dataset preprocessing utilities, located at tf.keras.preprocessing, help you go from raw data on disk to a tf.data.Dataset object that can be used to train a model. img_width, img_height = 320, 240. train_data_dir = 'data/train' VGG16 in TensorFlow. from tensorflow import keras. It is written in Python, though - so I adapted the code to R. Keras Preprocessing Layers Keras has preprocessing layers so that you can preprocess your data as part of a model. format) 10 print (img. # the data for training and the remaining 25% for testing. def preprocess (image_path): img = image.load_img (image_path, target_size= (299, 299)) x = image.img_to_array (img) x = np.expand_dims (x, axis=0) x = preprocess_input (x) return x. and I'm getting the following error: AttributeError: module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory' tensorflow version: 2.2.0 keras version: 2.3.0-tf. In my example train_cropped.py code, I used ImageDataGenerator.flow_from_directory() to resize all input images to (256, 256) and then use my own crop_generator to generate random (224, 224) crops from the resized images. The larger size is then cropped in order to produce a square image. from keras.applications.vgg16 import VGG16 model = VGG16(weights='imagenet', include_top=False) If you want to stick to the original preprocessing steps, you can find pre-trained weights that is trained with the original preprocessing steps instead of using the weights that comes with Keras. smart_resize: If True, the resizing function used will be tf.keras.preprocessing.image.smart_resize, which preserves the aspect ratio of the original image by using a mixture of resizing and cropping. Data preprocessing and data augmentation module of the Keras deep learning library … If we have a model that takes in an image as its input, and outputs class scores, i.e. Wow Classic Raid Spreadsheet, Computer Input And Output Devices Notes, Please Do Not Touch Magazine, Spraying Water On Burgers, The Impact Of Digital Transformation In The Finance Industry, Calgary Vinyl Records, What Did John Wesley Shipp Play In, Redbridge 11 Plus Common Catchment Area, " /> >> from keras.preprocessing.image import img_to_array >>> image = img_to_array (image) By now, we have the number representation of our image. Input (3) Output Execution Info Log Comments (0) Best Submission. We'll also see how we can work with MobileNets in code using TensorFlow's Keras API. keras. Successful. interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. In Keras, we have ImageDataGenerator API, which generates the images in batches with real-time data augmentation. Inception v3 in TensorFlow. This is Part 2 of a MNIST digit classification notebook. If we have a model that takes in an image as its input, and outputs class scores, i.e. from tensorflow import keras. Input pipeline using Tensorflow will create tensors as an input to the model. from keras_preprocessing.image import ImageDataGenerator from keras.layers import … import matplotlib.pyplot as plt import seaborn as sns import keras from keras.models import Sequential from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam from sklearn.metrics import classification_report,confusion_matrix import tensorflow as tf import cv2 import … json, xml, binary) and how your model is expecting it (e.g. Explaining Keras image classifier predictions with Grad-CAM¶. [ ] Setup [ ] [ ] import tensorflow as tf. These are the pixel values of the image stored in a 2D matrix. COCO animals dataset and pre-processing images. Looking to image preprocessing example in Keras, you often see image is scaled down by factor 255 before feeding to the model. Keras center and random crop support for ImageDataGenerator. It first resizes image preserving aspect ratio and then performs crop. Numpy will be used for creating a new dimension and Keras for preprocessing and importing the resnet50 pre-trained model. tf.keras.preprocessing.image_dataset_from_directory(. Transfer Learning in Keras (Image Recognition) Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Credit to Robin Smits and lafoss. from keras.layers import Conv2D, MaxPooling2D. Input PIL Image instance. We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation. image package. These input processing pipelines can be used as independent preprocessing code in non-Keras workflows, combined directly with Keras models, and exported as part of a Keras SavedModel. Here's a quick example: let's say you have 10 folders, each containing 10,000 images from a different category, and you want to train a classifier that maps an image to its category. Keras Keras2.0. Brightness_range Keras is an argument in ImageDataGenerator class of keras. Using the IMAGE_PATH we load the image and then construct the payload to the request. ページによって引数の説明があったりなかったりするので. Keras dataset preprocessing utilities, located at tf.keras.preprocessing, help you go from raw data on disk to a tf.data.Dataset object that can be used to train a model. img_width, img_height = 320, 240. train_data_dir = 'data/train' VGG16 in TensorFlow. from tensorflow import keras. It is written in Python, though - so I adapted the code to R. Keras Preprocessing Layers Keras has preprocessing layers so that you can preprocess your data as part of a model. format) 10 print (img. # the data for training and the remaining 25% for testing. def preprocess (image_path): img = image.load_img (image_path, target_size= (299, 299)) x = image.img_to_array (img) x = np.expand_dims (x, axis=0) x = preprocess_input (x) return x. and I'm getting the following error: AttributeError: module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory' tensorflow version: 2.2.0 keras version: 2.3.0-tf. In my example train_cropped.py code, I used ImageDataGenerator.flow_from_directory() to resize all input images to (256, 256) and then use my own crop_generator to generate random (224, 224) crops from the resized images. The larger size is then cropped in order to produce a square image. from keras.applications.vgg16 import VGG16 model = VGG16(weights='imagenet', include_top=False) If you want to stick to the original preprocessing steps, you can find pre-trained weights that is trained with the original preprocessing steps instead of using the weights that comes with Keras. smart_resize: If True, the resizing function used will be tf.keras.preprocessing.image.smart_resize, which preserves the aspect ratio of the original image by using a mixture of resizing and cropping. Data preprocessing and data augmentation module of the Keras deep learning library … If we have a model that takes in an image as its input, and outputs class scores, i.e. Wow Classic Raid Spreadsheet, Computer Input And Output Devices Notes, Please Do Not Touch Magazine, Spraying Water On Burgers, The Impact Of Digital Transformation In The Finance Industry, Calgary Vinyl Records, What Did John Wesley Shipp Play In, Redbridge 11 Plus Common Catchment Area, " />
To Top
Kezdőlap
Close
Bemutatkozás
Szakterület
Partnerek
Kapcsolat
13
jún 2021
No Comments
Egyéb
Permalink

keras preprocessing image

Pastebin.com is the number one paste tool since 2002. image import load_img 3 from keras. It first resizes image preserving aspect ratio and then performs crop. Deep neural networks and deep learning have become popular in past few years, thanks to the breakthroughs in research, starting from AlexNet, VGG, GoogleNet, and ResNet. Resized image size is based on crop_fraction which is hardcoded but can be changed. Let’s start with a few minor preprocessing steps. In Keras, there's an easy way to do data augmentation with the class tensorflow.keras.image.preprocessing.ImageDataGenerator. A while ago, I wrote two blogposts about image classification with Keras and about how to use your own models or pretrained models for predictions and using LIME to explain to predictions. Keras provides the ImageDataGenerator class that defines the configuration for image data preparation and augmentation. Deep Reinforcement Learning. MobileNet image classification with TensorFlow's Keras API. Keras Preprocessing is the data preprocessing and data augmentation module of the Keras deep learning library. View source on GitHub. probabilities that a certain object is present in the image, then we can use ELI5 to check what is it in the image that made the model predict a … preprocessing_function: function that will be applied on each input. Arguments: path: Path to image file; grayscale: Boolean, whether to load the image as grayscale. MNIST image classification with CNN & Keras. In Keras this can be done via the keras.preprocessing.image.ImageDataGenerator class. Deep Convolutional Neural Networks in deep learning take an hour or day to train the mode if the dataset we are playing is vast. For this project, I have imported numpy and Keras packages only. Like the rest of Keras, the image augmentation API is simple and powerful. Opinions. Private Score. Preprocessing the Training set. Pastebin is a website where you can store text online for a set period of time. Keras Image Processing API. Next step requires you to convert the image to numbers. Notice, to ImageDataGenerator for each of the data sets, we specify preprocessing_function=tf.keras.applications.vgg16.preprocess_input. The KERAS_REST_API_URL specifies our endpoint while the IMAGE_PATH is the path to our input image residing on disk. The Keras deep learning library provides utilities for working with image data. It provides utilities for working with image data, text data, and sequence data. In this tutorial we will see how to use MobileNetV2 pre trained model for image classification.MobileNetV2 is pre-trained on the ImageNet dataset. probabilities that a certain object is present in the image, then we can use ELI5 to check what is it in the image that made the model predict a certain class score. 1 # example of saving an image with the Keras API 2 from keras. image_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet') new_input = image_model.input hidden_layer = image_model.layers[-1].output Since each image is going to have a unique feature representation regardless of the epoch or iteration, it's recommended to run all the images through the feature extractor once and cache the extracted features on disk. The GPU utilization increased from ~10% to ~60%; If nothing from the above helps we can take a look at the code and see that keras does the img. image import save_img 4 # save the image with a new filename 5 save_img ('Keras_kolala.png', img_array) 6 # load the image to confirm it was saved correctly 7 img = load_img ('Keras_kolala.png') 8 print (type (img)) 9 print (img. In the next section, we will go over many of the image augmentation procedures that Keras provides. Preprocessing can be very tedious depending on the data format (e.g. The other library is Pytorch. If you never set it, then it will be "channels_last". It allows you to specify the augmentation parameters, which we will go over in the next steps. For more details, have a look at the Keras documentation for the ImageDataGenerator class. Data augmentation is one of the useful techniques in deep learning to improve the model training accuracy. You can also use keras.preprocessing to export augmented image files to a folder in order to build up a giant dataset of altered images should you desire to do so. from keras.preprocessing.image import ImageDataGenerator. Here I will be using Keras [1] to build a Convolutional Neural network for classifying hand written digits. Resize the image to match the input size for the Input layer of … ImageNet dataset. Recently, I came across this blogpost on using Keras to extract learned features from models and use those to cluster images. Image Preprocessing with Keras. #Image preprocessing in Keras from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img datagen = ImageDataGenerator (rotation_range = 45, width_shift_range = 0.3, height_shift_range = 0.3, shear_range = 0.3, zoom_range = 0.3, horizontal_flip = True, fill_mode = 'nearest') img = load_img ('iguana.jpg') # this is a PIL image x = img_to_array (img) # … In addition, We will also see how can we achieve Data Augmentation using brightness_range in Keras. class sklearn.preprocessing. Preprocessing the dataset for RNN models with Keras Building an RNN network in Keras is much simpler as compared to building using lower=level TensorFlow classes and methods. Explaining Keras image classifier predictions with Grad-CAM¶. Keras provides the ImageDataGenerator class for real-time data augmentation. Model ( base64_input, final_output) In this episode, we'll introduce MobileNets, a class of light weight deep convolutional neural networks that are vastly smaller in size and faster in performance than many other popular models. The following command install Keras within conda environment. More than 1 year has passed since last update. Standardize features by removing the mean and scaling to unit variance. For Keras, we preprocess the data, as described in the previous sections, to get the supervised machine learning time series datasets: X_train, Y_train, X_test, Y_test . Submitted by CodeNinja a year ago. System.String: dtype: Dtype to … from keras.models import Sequential """Import from keras_preprocessing not from keras.preprocessing, because Keras may or maynot contain the features discussed here depending upon when you read this article, until the keras_preprocessed library is updated in Keras use the github version.""" The larger size is then cropped in order to produce a square image. Here we have a JPEG file, so we use decode_jpeg () with three color channels. See Stable. However, Keras provides inbuilt methods that can perform this task easily. See Nightly. This class allows you to: configure random transformations and normalization operations to be done on your image data during training; instantiate generators of augmented image batches (and their labels) via .flow(data, labels) or .flow_from_directory(directory). 実際に動かしたトライをもとに … The function should take one argument: one image (NumPy tensor with rank 3), and should output a NumPy tensor with the same shape. Loads an image into PIL format. Input (3) Output Execution Info Log Comments (0) Best Submission. >>> from keras.preprocessing.image import img_to_array >>> image = img_to_array (image) By now, we have the number representation of our image. Input (3) Output Execution Info Log Comments (0) Best Submission. We'll also see how we can work with MobileNets in code using TensorFlow's Keras API. keras. Successful. interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. In Keras, we have ImageDataGenerator API, which generates the images in batches with real-time data augmentation. Inception v3 in TensorFlow. This is Part 2 of a MNIST digit classification notebook. If we have a model that takes in an image as its input, and outputs class scores, i.e. from tensorflow import keras. Input pipeline using Tensorflow will create tensors as an input to the model. from keras_preprocessing.image import ImageDataGenerator from keras.layers import … import matplotlib.pyplot as plt import seaborn as sns import keras from keras.models import Sequential from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout from keras.preprocessing.image import ImageDataGenerator from keras.optimizers import Adam from sklearn.metrics import classification_report,confusion_matrix import tensorflow as tf import cv2 import … json, xml, binary) and how your model is expecting it (e.g. Explaining Keras image classifier predictions with Grad-CAM¶. [ ] Setup [ ] [ ] import tensorflow as tf. These are the pixel values of the image stored in a 2D matrix. COCO animals dataset and pre-processing images. Looking to image preprocessing example in Keras, you often see image is scaled down by factor 255 before feeding to the model. Keras center and random crop support for ImageDataGenerator. It first resizes image preserving aspect ratio and then performs crop. Numpy will be used for creating a new dimension and Keras for preprocessing and importing the resnet50 pre-trained model. tf.keras.preprocessing.image_dataset_from_directory(. Transfer Learning in Keras (Image Recognition) Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Credit to Robin Smits and lafoss. from keras.layers import Conv2D, MaxPooling2D. Input PIL Image instance. We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation. image package. These input processing pipelines can be used as independent preprocessing code in non-Keras workflows, combined directly with Keras models, and exported as part of a Keras SavedModel. Here's a quick example: let's say you have 10 folders, each containing 10,000 images from a different category, and you want to train a classifier that maps an image to its category. Keras Keras2.0. Brightness_range Keras is an argument in ImageDataGenerator class of keras. Using the IMAGE_PATH we load the image and then construct the payload to the request. ページによって引数の説明があったりなかったりするので. Keras dataset preprocessing utilities, located at tf.keras.preprocessing, help you go from raw data on disk to a tf.data.Dataset object that can be used to train a model. img_width, img_height = 320, 240. train_data_dir = 'data/train' VGG16 in TensorFlow. from tensorflow import keras. It is written in Python, though - so I adapted the code to R. Keras Preprocessing Layers Keras has preprocessing layers so that you can preprocess your data as part of a model. format) 10 print (img. # the data for training and the remaining 25% for testing. def preprocess (image_path): img = image.load_img (image_path, target_size= (299, 299)) x = image.img_to_array (img) x = np.expand_dims (x, axis=0) x = preprocess_input (x) return x. and I'm getting the following error: AttributeError: module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory' tensorflow version: 2.2.0 keras version: 2.3.0-tf. In my example train_cropped.py code, I used ImageDataGenerator.flow_from_directory() to resize all input images to (256, 256) and then use my own crop_generator to generate random (224, 224) crops from the resized images. The larger size is then cropped in order to produce a square image. from keras.applications.vgg16 import VGG16 model = VGG16(weights='imagenet', include_top=False) If you want to stick to the original preprocessing steps, you can find pre-trained weights that is trained with the original preprocessing steps instead of using the weights that comes with Keras. smart_resize: If True, the resizing function used will be tf.keras.preprocessing.image.smart_resize, which preserves the aspect ratio of the original image by using a mixture of resizing and cropping. Data preprocessing and data augmentation module of the Keras deep learning library … If we have a model that takes in an image as its input, and outputs class scores, i.e.

Wow Classic Raid Spreadsheet, Computer Input And Output Devices Notes, Please Do Not Touch Magazine, Spraying Water On Burgers, The Impact Of Digital Transformation In The Finance Industry, Calgary Vinyl Records, What Did John Wesley Shipp Play In, Redbridge 11 Plus Common Catchment Area,

Vélemény, hozzászólás?

Az email címet nem tesszük közzé. A kötelező mezőket * karakterrel jelöljük.

0-24

Annak érdekében, hogy akár hétvégén vagy éjszaka is megfelelő védelemhez juthasson, telefonos ügyeletet tartok, melynek keretében bármikor hívhat, ha segítségre van szüksége.

 Tel.: +36702062206

×
Büntetőjog

Amennyiben Önt letartóztatják, előállítják, akkor egy meggondolatlan mondat vagy ésszerűtlen döntés később az eljárás folyamán óriási hátrányt okozhat Önnek.

Tapasztalatom szerint már a kihallgatás első percei is óriási pszichikai nyomást jelentenek a terhelt számára, pedig a „tiszta fejre” és meggondolt viselkedésre ilyenkor óriási szükség van. Ez az a helyzet, ahol Ön nem hibázhat, nem kockáztathat, nagyon fontos, hogy már elsőre jól döntsön!

Védőként én nem csupán segítek Önnek az eljárás folyamán az eljárási cselekmények elvégzésében (beadvány szerkesztés, jelenlét a kihallgatásokon stb.) hanem egy kézben tartva mérem fel lehetőségeit, kidolgozom védelmének precíz stratégiáit, majd ennek alapján határozom meg azt az eszközrendszert, amellyel végig képviselhetem Önt és eredményül elérhetem, hogy semmiképp ne érje indokolatlan hátrány a büntetőeljárás következményeként.

Védőügyvédjeként én nem csupán bástyaként védem érdekeit a hatóságokkal szemben és dolgozom védelmének stratégiáján, hanem nagy hangsúlyt fektetek az Ön folyamatos tájékoztatására, egyben enyhítve esetleges kilátástalannak tűnő helyzetét is.

×
Polgári jog

Jogi tanácsadás, ügyintézés. Peren kívüli megegyezések teljes körű lebonyolítása. Megállapodások, szerződések és az ezekhez kapcsolódó dokumentációk megszerkesztése, ellenjegyzése. Bíróságok és más hatóságok előtti teljes körű jogi képviselet különösen az alábbi területeken:

×
Ingatlanjog

Ingatlan tulajdonjogának átruházáshoz kapcsolódó szerződések (adásvétel, ajándékozás, csere, stb.) elkészítése és ügyvédi ellenjegyzése, valamint teljes körű jogi tanácsadás és földhivatal és adóhatóság előtti jogi képviselet.

Bérleti szerződések szerkesztése és ellenjegyzése.

Ingatlan átminősítése során jogi képviselet ellátása.

Közös tulajdonú ingatlanokkal kapcsolatos ügyek, jogviták, valamint a közös tulajdon megszüntetésével kapcsolatos ügyekben való jogi képviselet ellátása.

Társasház alapítása, alapító okiratok megszerkesztése, társasházak állandó és eseti jogi képviselete, jogi tanácsadás.

Ingatlanokhoz kapcsolódó haszonélvezeti-, használati-, szolgalmi jog alapítása vagy megszüntetése során jogi képviselet ellátása, ezekkel kapcsolatos okiratok szerkesztése.

Ingatlanokkal kapcsolatos birtokviták, valamint elbirtoklási ügyekben való ügyvédi képviselet.

Az illetékes földhivatalok előtti teljes körű képviselet és ügyintézés.

×
Társasági jog

Cégalapítási és változásbejegyzési eljárásban, továbbá végelszámolási eljárásban teljes körű jogi képviselet ellátása, okiratok szerkesztése és ellenjegyzése

Tulajdonrész, illetve üzletrész adásvételi szerződések megszerkesztése és ügyvédi ellenjegyzése.

×
Állandó, komplex képviselet

Még mindig él a cégvezetőkben az a tévképzet, hogy ügyvédet választani egy vállalkozás vagy társaság számára elegendő akkor, ha bíróságra kell menni.

Semmivel sem árthat annyit cége nehezen elért sikereinek, mint, ha megfelelő jogi képviselet nélkül hagyná vállalatát!

Irodámban egyedi megállapodás alapján lehetőség van állandó megbízás megkötésére, melynek keretében folyamatosan együtt tudunk működni, bármilyen felmerülő kérdés probléma esetén kereshet személyesen vagy telefonon is.  Ennek nem csupán az az előnye, hogy Ön állandó ügyfelemként előnyt élvez majd időpont-egyeztetéskor, hanem ennél sokkal fontosabb, hogy az Ön cégét megismerve személyesen kezeskedem arról, hogy tevékenysége folyamatosan a törvényesség talaján maradjon. Megismerve az Ön cégének munkafolyamatait és folyamatosan együttműködve vezetőséggel a jogi tudást igénylő helyzeteket nem csupán utólag tudjuk kezelni, akkor, amikor már „ég a ház”, hanem előre felkészülve gondoskodhatunk arról, hogy Önt ne érhesse meglepetés.

×