a Sic!F @sbdZddlZddlZddlZddlZddlmmZ ddl m Z ddl m Z z2ddlmZz ejZWney|eZYn0WneydZdZYn0edurejejejejejejdZe jjZejejejejej ej!ej"ej#dZ$e dgd d#d d Z%d dZ&e ddd$ddZ'e ddd%ddZ(e ddd&ddZ)e ddd'd!d"Z*dS)(z$Utilities related to image handling.N)backend) keras_export)Image)nearestbilinearbicubichammingboxZlanczos)rrrarealanczos3lanczos5gaussian mitchellcubicz&keras.preprocessing.image.smart_resize)v1rcCst|dkrtd|dt|}|jjdur\|jjdksJ|jjdkr\td|jdt|}|d|d }}|\}}|jjdur|jd } nd} tt||d |d } tt||d |d } t|| } t|| } tt|| d dd } tt|| d dd } |jjdkrXtd | | d g}td | | d g}n t| | d g}t| | d g}t |||}tj j |||d}|jjdur|jjdkr| ddd| f|jjdkr| dd| ft |tjr|S|S)a Resize images to a target size without aspect ratio distortion. Warning: `tf.keras.preprocessing.image.smart_resize` is not recommended for new code. Prefer `tf.keras.layers.Resizing`, which provides the same functionality as a preprocessing layer and adds `tf.RaggedTensor` support. See the [preprocessing layer guide]( https://www.tensorflow.org/guide/keras/preprocessing_layers) for an overview of preprocessing layers. TensorFlow image datasets typically yield images that have each a different size. However, these images need to be batched before they can be processed by Keras layers. To be batched, images need to share the same height and width. You could simply do: ```python size = (200, 200) ds = ds.map(lambda img: tf.image.resize(img, size)) ``` However, if you do this, you distort the aspect ratio of your images, since in general they do not all have the same aspect ratio as `size`. This is fine in many cases, but not always (e.g. for GANs this can be a problem). Note that passing the argument `preserve_aspect_ratio=True` to `resize` will preserve the aspect ratio, but at the cost of no longer respecting the provided target size. Because `tf.image.resize` doesn't crop images, your output images will still have different sizes. This calls for: ```python size = (200, 200) ds = ds.map(lambda img: smart_resize(img, size)) ``` Your output images will actually be `(200, 200)`, and will not be distorted. Instead, the parts of the image that do not fit within the target size get cropped out. The resizing process is: 1. Take the largest centered crop of the image that has the same aspect ratio as the target size. For instance, if `size=(200, 200)` and the input image has size `(340, 500)`, we take a crop of `(340, 340)` centered along the width. 2. Resize the cropped image to the target size. In the example above, we resize the `(340, 340)` crop to `(200, 200)`. Args: x: Input image or batch of images (as a tensor or NumPy array). Must be in format `(height, width, channels)` or `(batch_size, height, width, channels)`. size: Tuple of `(height, width)` integer. Target size. interpolation: String, interpolation to use for resizing. Defaults to `'bilinear'`. Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`. Returns: Array with shape `(size[0], size[1], channels)`. If the input image was a NumPy array, the output is a NumPy array, and if it was a TF tensor, the output is a TF tensor. z6Expected `size` to be a tuple of 2 integers, but got: .NzExpected an image array with shape `(height, width, channels)`, or `(batch_size, height, width, channels)`, but got input with incorrect rank, of shape float32int32r)imagessizemethod)len ValueErrortfconvert_to_tensorshaperankcastminimumstacksliceimageresize set_shape isinstancenpndarraynumpy)xr interpolationimgr heightwidth target_height target_widthZstatic_num_channels crop_height crop_widthcrop_box_hstartcrop_box_wstartZcrop_box_startZ crop_box_sizer8S/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/utils/image_utils.py smart_resizeBs^B         r:cCs,|}|tvr$td|tt|S)NzFValue not recognized for `interpolation`: {}. Supported values are: {})lower_TF_INTERPOLATION_METHODSNotImplementedErrorformatkeys)r.r8r8r9get_interpolationsr@zkeras.utils.array_to_imgz&keras.preprocessing.image.array_to_imgTcCsx|durt}|dur t}tdur0tdtj||d}|jdkrXtd|j |dvrntd||dkr| d d d }|r|t |}t |}|d kr||}|d 9}|j d d krt |ddS|j d dkrt |ddS|j d d kr`t |d krchannels_first channels_lastzInvalid data_format: rCrrruint8RGBARGBrILzUnsupported channel number: )rimage_data_formatfloatx pil_image ImportErrorr*asarrayndimrr transposeminmaxZ fromarrayastype)r- data_formatscalerBx_maxr8r8r9 array_to_imgsD#  $$rYzkeras.utils.img_to_arrayz&keras.preprocessing.image.img_to_arraycCs|durt}|dur t}|dvr6td|tj||d}t|jdkrj|dkr|ddd }nbt|jdkr|dkr| d |jd|jd f}q| |jd|jd d f}ntd |j|S) aConverts a PIL Image instance to a Numpy array. Usage: ```python from PIL import Image img_data = np.random.random(size=(100, 100, 3)) img = tf.keras.preprocessing.image.array_to_img(img_data) array = tf.keras.preprocessing.image.img_to_array(img) ``` Args: img: Input PIL Image instance. data_format: Image data format, can be either `"channels_first"` or `"channels_last"`. Defaults to `None`, in which case the global setting `tf.keras.backend.image_data_format()` is used (unless you changed it, it defaults to `"channels_last"`). dtype: Dtype to use. Default to `None`, in which case the global setting `tf.keras.backend.floatx()` is used (unless you changed it, it defaults to `"float32"`). Returns: A 3D Numpy array. Raises: ValueError: if invalid `img` or `data_format` is passed. N>rCrDzUnknown data_format: rArrCrrrEzUnsupported image shape: ) rrLrMrr*rPrr rRreshape)r/rVrBr-r8r8r9 img_to_arrays !r[zkeras.utils.save_imgz"keras.preprocessing.image.save_imgcKsf|durt}t|||d}|jdkrL|dks8|dkrLtd|d}|j|fd|i|dS) ajSaves an image stored as a Numpy array to a path or file object. Args: path: Path or file object. x: Numpy array. data_format: Image data format, either `"channels_first"` or `"channels_last"`. file_format: Optional file format override. If omitted, the format to use is determined from the filename extension. If a file object was used instead of a filename, this parameter should always be used. scale: Whether to rescale image values to be within `[0, 255]`. **kwargs: Additional keyword arguments passed to `PIL.Image.save()`. N)rVrWrHjpgjpegz?The JPG format does not support RGBA images, converting to RGB.rIr>)rrLrYmodewarningswarnconvertsave)pathr-rV file_formatrWkwargsr/r8r8r9save_imgRs rfzkeras.utils.load_imgz"keras.preprocessing.image.load_imgFrgbrcCs|rtdd}tdur"tdt|tjr:t|}n|t|tj t t frt|tj rdt | }t|d$}tt| }Wdq1s0Yntdt||dkr|jdvr|d}nH|d kr|jd kr|d }n*|d kr|jd kr|d }ntd |dur|d|df}|j|kr|tvrftd|dtt|} |r|j\} } |\} } | | | }| | | }t| |}t| |}| |d}| |d}||}||}||||g}|j|| |d}n ||| }|S)aLoads an image into PIL format. Usage: ``` image = tf.keras.preprocessing.image.load_img(image_path) input_arr = tf.keras.preprocessing.image.img_to_array(image) input_arr = np.array([input_arr]) # Convert single image to a batch. predictions = model.predict(input_arr) ``` Args: path: Path to image file. grayscale: DEPRECATED use `color_mode="grayscale"`. color_mode: One of `"grayscale"`, `"rgb"`, `"rgba"`. Default: `"rgb"`. The desired image format. target_size: Either `None` (default to original size) or tuple of ints `(img_height, img_width)`. interpolation: Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are `"nearest"`, `"bilinear"`, and `"bicubic"`. If PIL version 1.1.3 or newer is installed, `"lanczos"` is also supported. If PIL version 3.4.0 or newer is installed, `"box"` and `"hamming"` are also supported. By default, `"nearest"` is used. keep_aspect_ratio: Boolean, whether to resize images to a target size without aspect ratio distortion. The image is cropped in the center with target aspect ratio before resizing. Returns: A PIL Image instance. Raises: ImportError: if PIL is not available. ValueError: if interpolation method is not supported. ztyper^rarr_PIL_INTERPOLATION_METHODSjoinr?rSr')rcrh color_mode target_sizer.Zkeep_aspect_ratior/fZwidth_height_tupleresampler1r0r3r2r4r5r6r7Z crop_box_wendZ crop_box_hendZcrop_boxr8r8r9load_imglsz,     4                   r|)r)NTN)NN)NNT)FrgNrF)+__doc__rkrnr_r,r*tensorflow.compat.v2compatv2rkerasr tensorflow.python.util.tf_exportrPILrrNZ ResamplingZpil_image_resamplingAttributeErrorrOZNEARESTBILINEARBICUBICZHAMMINGBOXZLANCZOSrvr& ResizeMethodNEAREST_NEIGHBORAREALANCZOS3LANCZOS5GAUSSIAN MITCHELLCUBICr<r:r@rYr[rfr|r8r8r8r9sl          |  N 5