6biE SrSSKrSSKrSSKrSSKrSSKJs Jr SSK J r SSK J r SSKJr \R"r\bE\R*\R,\R.\R0\R2\R4S.r\ R8R:r\R,\R<\R.\R>\R@\RB\RD\RFS.r$\ "S/S 9SS j5r%S r&\ "S S 5SSj5r'\ "SS5SSj5r(\ "SS5SSj5r)\ "SS5SSj5r*g!\a \rGN f=f!\a SrSrGN1f=f)z$Utilities related to image handling.N)backend) keras_export)Image)nearestbilinearbicubichammingboxlanczos)rrrarealanczos3lanczos5gaussian mitchellcubicz&keras.preprocessing.image.smart_resize)v1cX[U5S:wa[SUS35e[R"U5nURR bMURR S:dURR S:a[SURS35e[R"U5nUSUS peUupxURR bURS n OSn [R "[R "Xg-S 5U- S 5n [R "[R "XX-S 5U- S 5n [R"XZ5n [R"Xk5n [R "[R "XZ- S 5S- S 5n [R "[R "Xk- S 5S- S 5n URR S:Xa3[R"S XS /5n[R"S XS /5nO0[R"XS /5n[R"XS /5n[R"X>U5n[RRX1US9nURR b]URR S:XaURSSSU 45 URR S:XaURSSU 45 [U[R5(aUR!5$U$)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/tf_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 TF-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. Supports `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`, `gaussian`, `mitchellcubic`. Defaults to `'bilinear'`. 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_widthstatic_num_channels crop_height crop_widthcrop_box_hstartcrop_box_wstartcrop_box_start crop_box_sizes X/home/james-whalen/.local/lib/python3.13/site-packages/tf_keras/src/utils/image_utils.py smart_resizer?BsD 4yA~DTF! L    q !C yy~~! 99>>A !!3;;>99+QH  HHSME"IuRyE"&M yy~~!!iim"'' %y1L@'K %y1MA7J **V1KE.Jgg $i014gOgg "I.2GO yy~~1o"JK"kr!BC ?Q"GH+2!>? ((3 6C ((// / FC yy~~! 99>>Q  MM4t-@A B 99>>Q  MM4':; <!RZZ  yy{ JcUR5nU[;a-[SRU[R 555e[U$)NzFValue not recognized for `interpolation`: {}. Supported values are: {})lower_TF_INTERPOLATION_METHODSNotImplementedErrorformatkeys)r1s r>get_interpolationrGsN!'')M55! f],E,J,J,LM   %] 33r@zkeras.utils.array_to_imgz&keras.preprocessing.image.array_to_imgcUc[R"5nUc[R"5n[c [ S5e[ R "XS9nURS:wa[SUR35eUS;a[SU35eUS:XaURS S S 5nU(a>U[ R"U5- n[ R"U5nUS :waX-nUS -nURS S :Xa&[R"URS5S5$URS S:Xa&[R"URS5S5$URS S :Xaz[ R"U5S :a0[R"USS2SS2S 4RS5S5$[R"USS2SS2S 4RS5S5$[SURS 35e)aConverts a 3D Numpy array to a PIL Image instance. Usage: ```python from PIL import Image img = np.random.random(size=(100, 100, 3)) pil_img = tf.keras.utils.array_to_img(img) ``` Args: x: Input data, in any form that can be converted to a Numpy array. data_format: Image data format, can be either `"channels_first"` or `"channels_last"`. None means the global setting `tf.keras.backend.image_data_format()` is used (unless you changed it, it uses `"channels_last"`). Defaults to `None`. scale: Whether to rescale the image such that minimum and maximum values are 0 and 255 respectively. Defaults to `True`. dtype: Dtype to use. None makes the global setting `tf.keras.backend.floatx()` to be used (unless you changed it, it uses `"float32"`). Defaults to `None`. Returns: A PIL Image instance. Raises: ImportError: if PIL is not available. ValueError: if invalid `x` or `data_format` is passed. NzCCould not import PIL.Image. The use of `array_to_img` requires PIL.dtyperzJExpected image array to have rank 3 (single image). Got array with shape:  channels_lastchannels_firstzInvalid data_format: rMrrruint8RGBARGBrILzUnsupported channel number: )rimage_data_formatfloatx pil_image ImportErrorr-asarrayndimr r# transposeminmax fromarrayastype)r0 data_formatscalerJx_maxs r> array_to_imgrcsF//1  }  6   1"Avv{ %%&WWI /  ==0 >?? && KK1a  q Mq  A: JA SwwqzQ""188G#4f== q""188G#4e<< q 66!9s?&&qAqz'8'8'A3G G""1Q1W:#4#4W#=sCC7 |DEEr@zkeras.utils.img_to_arrayz&keras.preprocessing.image.img_to_arraycJUc[R"5nUc[R"5nUS;a[SU35e[R "XS9n[ UR5S:XaUS:XaURSSS5nU$[ UR5S:XagUS:Xa0URSURSURS45nU$URURSURSS45nU$[S UR35e) a'Converts a PIL Image instance to a Numpy array. Usage: ```python from PIL import Image img_data = np.random.random(size=(100, 100, 3)) img = tf.keras.utils.array_to_img(img_data) array = tf.keras.utils.image.img_to_array(img) ``` Args: img: Input PIL Image instance. data_format: Image data format, can be either `"channels_first"` or `"channels_last"`. None means the global setting `tf.keras.backend.image_data_format()` is used (unless you changed it, it uses `"channels_last"`). Defaults to `None`. dtype: Dtype to use. None makes the global setting `tf.keras.backend.floatx()` to be used (unless you changed it, it uses `"float32"`). Defaults to `None`. Returns: A 3D Numpy array. Raises: ValueError: if invalid `img` or `data_format` is passed. rKzUnknown data_format: rIrrMrrrNzUnsupported image shape: ) rrUrVr r-rYrr#r[reshape)r2r`rJr0s r> img_to_arrayrfsB//1  } ==0 >?? 3$A 177|q * * Aq!$A H QWW  * * 1aggaj!''!*56A H 1771:qwwqz156A H4QWWI>??r@zkeras.utils.save_imgz"keras.preprocessing.image.save_imgc Uc[R"5n[XUS9nURS:Xa3US:XdUS:Xa'[R "S5 UR S5nUR"U4SU0UD6 g) a>Saves an image stored as a Numpy array to a path or file object. Args: path: Path or file object. x: Numpy array. data_format: Image data format, either `"channels_first"` or `"channels_last"`. file_format: Optional file format override. If omitted, the format to use is determined from the filename extension. If a file object was used instead of a filename, this parameter should always be used. scale: Whether to rescale image values to be within `[0, 255]`. **kwargs: Additional keyword arguments passed to `PIL.Image.save()`. N)r`rarQjpgjpegz?The JPG format does not support RGBA images, converting to RGB.rRrE)rrUrcmodewarningswarnconvertsave)pathr0r` file_formatrakwargsr2s r>save_imgrrRss//1 q ?C xx6{e3{f7L M kk% HHT0+00r@zkeras.utils.load_imgz"keras.preprocessing.image.load_imgc `U(a[R"S5 Sn[c [S5e[ U[ R 5(a[R"U5nO[ U[R[[45(a[ U[R5(a[UR55n[US5n[R"[ R "UR555nSSS5 O[S[U535eUS:Xa"WR S;aUR#S5nO[US :Xa"WR S :waUR#S 5nO3US :Xa"WR S :waUR#S 5nO [%S 5eUbUSUS4nUR&U:waU[(;a<[%SR+USR-[(R/5555e[(Un U(afUR&upUupX-U -nX-U -n[1X5n[1X5nX- S-nX- S-nUU-nUU-nUUUU/nUR3XUS9nU$UR3X5nU$!,(df  GN~=f)a:Loads an image into PIL format. Usage: ```python image = tf.keras.utils.load_img(image_path) input_arr = tf.keras.utils.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. zload_imgrlsX J !  M  $ ##nnT" D7<<4 5 5 dGLL ) )t||~&D $ ..AFFH!56C :4:, G  [  88- -++c"C v  88v ++f%C u  88u ++e$CKLL)!nk!n= 88) )$>> %%+V% "<"A"A"CD&2-@H # .@+ $4E $3 E "&6  3 #)#7A"=#(#5!"; /* < /+ = ##!!  jj!38jL Jjj!3> Ju s 9J J-)r)NTN)NN)NNT)FrwNrF)+__doc__rxr{rkr/r-tensorflow.compat.v2compatv2r! tf_keras.srcr tensorflow.python.util.tf_exportrPILrrW Resamplingpil_image_resamplingAttributeErrorrXNEARESTBILINEARBICUBICHAMMINGBOXLANCZOSrr) ResizeMethodNEAREST_NEIGHBORAREALANCZOS3LANCZOS5GAUSSIAN MITCHELLCUBICrCr?rGrcrfrrrr@r>rs+ !! :  &)(33#'//(11'//'//#'''// "xx$$ %%,,##   %%%%%%!// 62>y?yx4 HKFKF\ H2 2 j$&JK1L12$&JK sLsU )() I s.E EEE EE E.-E.