Upload processor

image_processing_evabyte.py

@@ -0,0 +1,204 @@
+# coding=utf-8
+"""Image processor class for EvaByte."""
+
+from typing import Dict, List, Optional, Union, Tuple
+
+import io
+from transformers.image_processing_utils import BaseImageProcessor
+from transformers.image_utils import (
+    ImageInput,
+    PILImageResampling,
+    valid_images,
+    validate_preprocess_arguments,
+)
+from PIL import Image
+
+def _get_qtable_bytes():
+    return {
+        5: b'\xff\xd8\xff\xdb\x00C\x00\xa0nx\x8cxd\xa0\x8c\x82\x8c\xb4\xaa\xa0\xbe\xf0\xff\xff\xf0\xdc\xdc\xf0\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xdb\x00C\x01\xa0\xb4\xb4\xf0\xd2\xf0\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xd9',
+        10: b'\xff\xd8\xff\xdb\x00C\x00P7<F<2PFAFZUP_x\xc8\x82xnnx\xf5\xaf\xb9\x91\xc8\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xdb\x00C\x01PZZxix\xeb\x82\x82\xeb\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xd9',
+        15: b'\xff\xd8\xff\xdb\x00C\x005%(/(!5/+/<95?P\x85WPIIP\xa3u{a\x85\xc1\xaa\xcb\xc8\xbe\xaa\xba\xb7\xd5\xf0\xff\xff\xd5\xe2\xff\xe6\xb7\xba\xff\xff\xff\xff\xff\xff\xff\xff\xff\xce\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xdb\x00C\x015<<PFP\x9dWW\x9d\xff\xdc\xba\xdc\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xd9',
+        20: b'\xff\xd8\xff\xdb\x00C\x00(\x1c\x1e#\x1e\x19(#!#-+(0<dA<77<{X]Id\x91\x80\x99\x96\x8f\x80\x8c\x8a\xa0\xb4\xe6\xc3\xa0\xaa\xda\xad\x8a\x8c\xc8\xff\xcb\xda\xee\xf5\xff\xff\xff\x9b\xc1\xff\xff\xff\xfa\xff\xe6\xfd\xff\xf8\xff\xdb\x00C\x01(--<5<vAAv\xf8\xa5\x8c\xa5\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xf8\xff\xd9',
+        25: b'\xff\xd8\xff\xdb\x00C\x00 \x16\x18\x1c\x18\x14 \x1c\x1a\x1c$" &0P40,,0bFJ:Ptfzxrfpn\x80\x90\xb8\x9c\x80\x88\xae\x8anp\xa0\xda\xa2\xae\xbe\xc4\xce\xd0\xce|\x9a\xe2\xf2\xe0\xc8\xf0\xb8\xca\xce\xc6\xff\xdb\x00C\x01 $$0*0^44^\xc6\x84p\x84\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xc6\xff\xd9',
+        30: b'\xff\xd8\xff\xdb\x00C\x00\x1b\x12\x14\x17\x14\x11\x1b\x17\x16\x17\x1e\x1c\x1b (B+(%%(Q:=0B`Ued_U][jx\x99\x81jq\x90s[]\x85\xb5\x86\x90\x9e\xa3\xab\xad\xabg\x80\xbc\xc9\xba\xa6\xc7\x99\xa8\xab\xa4\xff\xdb\x00C\x01\x1b\x1e\x1e(#(N++N\xa4n]n\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xa4\xff\xd9',
+        50: b'\xff\xd8\xff\xdb\x00C\x00\x10\x0b\x0c\x0e\x0c\n\x10\x0e\r\x0e\x12\x11\x10\x13\x18(\x1a\x18\x16\x16\x181#%\x1d(:3=<9387@H\\N@DWE78PmQW_bghg>Mqypdx\\egc\xff\xdb\x00C\x01\x10\x12\x12\x18\x15\x18/\x1a\x1a/cB8Bcccccccccccccccccccccccccccccccccccccccccccccccccc\xff\xd9',
+        75: b'\xff\xd8\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\x1d\x1a\x1c\x1c $.\' ",#\x1c\x1c(7),01444\x1f\'9=82<.342\xff\xdb\x00C\x01\x08\t\t\x0c\x0b\x0c\x18\r\r\x182!\x1c!22222222222222222222222222222222222222222222222222\xff\xd9',
+        95: b'\xff\xd8\xff\xdb\x00C\x00\x02\x01\x01\x01\x01\x01\x02\x01\x01\x01\x02\x02\x02\x02\x02\x04\x03\x02\x02\x02\x02\x05\x04\x04\x03\x04\x06\x05\x06\x06\x06\x05\x06\x06\x06\x07\t\x08\x06\x07\t\x07\x06\x06\x08\x0b\x08\t\n\n\n\n\n\x06\x08\x0b\x0c\x0b\n\x0c\t\n\n\n\xff\xdb\x00C\x01\x02\x02\x02\x02\x02\x02\x05\x03\x03\x05\n\x07\x06\x07\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\xff\xd9',
+        100: b'\xff\xd8\xff\xdb\x00C\x00\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\xff\xdb\x00C\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\xff\xd9',
+    }
+
+
+def _resize_if_exceeding_max_len(
+    width: int, height: int, min_len: Optional[int] = 16, max_len: Optional[int] = None
+) -> Tuple[int, int]:
+    """
+    Get the output size of the image after resizing given a dictionary specifying the max and min sizes.
+
+    Args:
+        height (`int`):
+            Height of the input image.
+        width (`int`):
+            Width of the input image.
+        max_len (`Dict[str, int]`, *optional*, defaults to the maximum size of the image):
+            Defines the maximum dimensions of the image.
+
+    Returns:
+        The output size of the image after resizing.
+    """
+    max_len = max(height, width) if max_len is None else max_len
+    aspect_ratio = width / height
+
+    if width >= height and width > max_len:
+        width = max_len
+        height = int(width / aspect_ratio)
+        if height % 2 != 0:
+            height += 1
+    elif height > width and height > max_len:
+        height = max_len
+        width = int(height * aspect_ratio)
+        if width % 2 != 0:
+            width += 1
+
+    # Avoid resizing to a size smaller than 1
+    height = max(height, min_len)
+    width = max(width, min_len)
+    return width, height
+
+class EvaByteImageProcessor(BaseImageProcessor):
+
+    model_input_names = []
+    
+    def __init__(
+        self,
+        do_resize: bool = True,
+        resample: PILImageResampling = PILImageResampling.LANCZOS,
+        size: Dict[str, int] = None,
+        do_convert_rgb: bool = True,
+        jpeg_quality: int = 25,
+        jpeg_subsampling: str = "4:2:0",
+        jpeg_streamtype: str = 2,
+        jpeg_restart_marker_blocks: int = 1,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.do_resize = do_resize
+        self.resample = resample
+        self.size = size if size is not None else {"longest_edge": 384}
+        self.do_convert_rgb = do_convert_rgb
+        self.jpeg_quality = jpeg_quality
+        self.jpeg_subsampling = jpeg_subsampling
+        self.jpeg_streamtype = jpeg_streamtype
+        self.jpeg_restart_marker_blocks = jpeg_restart_marker_blocks
+
+    def jpeg_encode(
+        self, 
+        image,
+        jpeg_quality,
+        jpeg_subsampling,
+        jpeg_streamtype,
+        jpeg_restart_marker_blocks,
+    ):
+        with io.BytesIO() as output:
+            image.save(
+                output, 
+                format="JPEG",
+                quality=jpeg_quality, 
+                subsampling=jpeg_subsampling, 
+                streamtype=jpeg_streamtype, 
+                restart_marker_blocks=jpeg_restart_marker_blocks
+            )
+            jpeg_bytes = output.getvalue()
+        return jpeg_bytes
+
+    def jpeg_merge_qtables(
+        self,
+        image_bytes,
+        jpeg_quality=None,
+    ):
+        if jpeg_quality is None:
+            jpeg_quality = self.jpeg_quality
+        qtable_bytes = _get_qtable_bytes()[jpeg_quality]
+        return image_bytes[:2] + qtable_bytes[2:-2] + image_bytes[2:]
+        
+    def resize(
+        self,
+        image: Image,
+        size: Dict[str, int],
+        resample: PILImageResampling = PILImageResampling.LANCZOS,
+    ) -> Image:
+        if "longest_edge" in size:
+            width, height = image.size
+            # Find the output size, when rescaling the longest edge to max_len and preserving the aspect ratio
+            width, height = _resize_if_exceeding_max_len(width, height, max_len=size["longest_edge"])
+            size = (width, height)
+        elif "width" in size and "height" in size:
+            size = (size["width"], size["height"])
+        else:
+            raise ValueError("size must be a dictionary with key 'longest_edge' or 'height' and 'width'.")
+        resized_image = image.resize(size, resample=resample)
+        return resized_image
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: bool = None,
+        resample = None,
+        size: Dict[str, int] = None,
+        do_convert_rgb: bool = None,
+        jpeg_quality: int = None,
+        jpeg_subsampling: str = None,
+        jpeg_streamtype: str = None,
+        jpeg_restart_marker_blocks: int = None,
+    ):
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        resample = resample if resample is not None else self.resample
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+        jpeg_quality = jpeg_quality if jpeg_quality is not None else self.jpeg_quality
+        jpeg_subsampling = jpeg_subsampling if jpeg_subsampling is not None else self.jpeg_subsampling
+        jpeg_streamtype = jpeg_streamtype if jpeg_streamtype is not None else self.jpeg_streamtype
+        jpeg_restart_marker_blocks = jpeg_restart_marker_blocks if jpeg_restart_marker_blocks is not None else self.jpeg_restart_marker_blocks
+
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        validate_preprocess_arguments(
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+        images_list = images
+        if do_convert_rgb:
+            images_list = [
+                [
+                    image.convert("RGB") for image in images
+                ]
+                for images in images_list
+            ]
+
+        if do_resize:
+            images_list = [
+                [
+                    self.resize(image=image, size=size, resample=resample)
+                    for image in images
+                ]
+                for images in images_list
+            ]
+
+        jpeg_bytes = [
+            [
+                self.jpeg_encode(
+                    image,
+                    jpeg_quality,
+                    jpeg_subsampling,
+                    jpeg_streamtype,
+                    jpeg_restart_marker_blocks
+                ) for image in images
+            ]
+            for images in images_list
+        ]
+        return jpeg_bytes

preprocessor_config.json

@@ -0,0 +1,16 @@
+{
+  "auto_map": {
+    "AutoImageProcessor": "image_processing_evabyte.EvaByteImageProcessor"
+  },
+  "do_convert_rgb": true,
+  "do_resize": true,
+  "image_processor_type": "EvaByteImageProcessor",
+  "jpeg_quality": 25,
+  "jpeg_restart_marker_blocks": 1,
+  "jpeg_streamtype": 2,
+  "jpeg_subsampling": "4:2:0",
+  "resample": 1,
+  "size": {
+    "longest_edge": 384
+  }
+}