support cropping animated images (gif+webp)

src/bma_client_lib/bma_client.py

@@ -25,6 +25,7 @@
     ThumbnailJob,
     ThumbnailSourceJob,
 )
+from .pillow_resize_and_crop import transform_image
 
 logger = logging.getLogger("bma_client")
 
@@ -248,7 +249,7 @@ def write_and_upload_result(self, job: Job, result: "JobResult", filename: str)
             metadata: dict[str, int | str] = {}
             if isinstance(job, ImageConversionJob | ThumbnailJob):
                 image, exif = result
-                if not isinstance(image, Image.Image) or not isinstance(exif, Image.Exif):
+                if not isinstance(image[0], Image.Image) or not isinstance(exif, Image.Exif):
                     raise TypeError("Fuck")
                 # apply format specific encoding options
                 kwargs = {}
@@ -258,18 +259,30 @@ def write_and_upload_result(self, job: Job, result: "JobResult", filename: str)
                     logger.debug(f"Format {job.mimetype} has custom encoding settings, kwargs is now: {kwargs}")
                 else:
                     logger.debug(f"No custom settings for format {job.mimetype}")
-                image.save(buf, format=job.filetype, exif=exif, **kwargs)
+                # sequence?
+                if len(image) > 1:
+                    kwargs["append_images"] = image[1:]
+                    kwargs["save_all"] = True
+                image[0].save(buf, format=job.filetype, exif=exif, **kwargs)
 
             elif isinstance(job, ImageExifExtractionJob):
                 logger.debug(f"Got exif data {result}")
                 buf.write(json.dumps(result).encode())
 
             elif isinstance(job, ThumbnailSourceJob):
                 image, exif = result
-                if not isinstance(image, Image.Image) or not isinstance(exif, Image.Exif):
+                if not isinstance(image[0], Image.Image) or not isinstance(exif, Image.Exif):
                     raise TypeError("Fuck")
-                image.save(buf, format="WEBP", lossless=True, quality=1)
-                metadata = {"width": 500, "height": image.height, "mimetype": "image/webp"}
+                kwargs = {}
+                # thumbnailsources are always WEBP
+                if "image/webp" in self.settings["encoding"]["images"]:
+                    kwargs.update(self.settings["encoding"]["images"]["image/webp"])
+                # sequence?
+                if len(image) > 1:
+                    kwargs["append_images"] = image[1:]
+                    kwargs["save_all"] = True
+                image[0].save(buf, format="WEBP", **kwargs)
+                metadata = {"width": 500, "height": image[0].height, "mimetype": "image/webp"}
 
             else:
                 logger.error("Unsupported job type")
@@ -315,15 +328,11 @@ def handle_image_conversion_job(
 
         logger.debug(f"Desired image size is {size}, aspect ratio: {ratio} ({orig_str}), converting image...")
         start = time.time()
-        # custom AR or not?
-        if job.custom_aspect_ratio:
-            image = ImageOps.fit(image=image, size=size, method=Image.Resampling.LANCZOS, centering=crop_center)  # type: ignore[assignment]
-        else:
-            image.thumbnail(size=size, resample=Image.Resampling.LANCZOS)
+        images = transform_image(original_img=image, crop_w=size[0], crop_h=size[1])
         logger.debug(f"Converting image size and AR took {time.time() - start} seconds")
 
         logger.debug("Done, returning result...")
-        return image, exif
+        return images, exif
 
     def upload_job_result(
         self,

src/bma_client_lib/datastructures.py

@@ -4,7 +4,7 @@
 from dataclasses import dataclass
 from typing import TypeAlias
 
-from PIL import Image
+from PIL import Image, ImageFile
 
 
 @dataclass
@@ -20,6 +20,7 @@ class BaseJob:
     finished: bool
     source_url: str
     source_filename: str
+    source_mimetype: str
     schema_name: str
 
 
@@ -54,7 +55,7 @@ class ThumbnailJob(ImageConversionJob):
     "ThumbnailJob": ThumbnailJob,
 }
 
-ImageConversionJobResult: TypeAlias = tuple[Image.Image, Image.Exif]
+ImageConversionJobResult: TypeAlias = tuple[list[Image.Image | ImageFile.ImageFile], Image.Exif]
 ThumbnailSourceJobResult: TypeAlias = ImageConversionJobResult
 ExifExtractionJobResult: TypeAlias = dict[str, dict[str, str]]
 JobResult: TypeAlias = ImageConversionJobResult | ExifExtractionJobResult | ThumbnailSourceJobResult

src/bma_client_lib/pillow_resize_and_crop.py

@@ -0,0 +1,66 @@
+"""Pillow cropping with sequence (gif, webp) support.
+
+Borrowed from https://gist.github.com/muratgozel/ce1aa99f97fc1a99b3f3ec90cf77e5f5
+"""
+
+from math import fabs, floor
+
+from PIL import Image, ImageFile, ImageSequence
+
+
+def transform_image(original_img: Image.Image, crop_w: int, crop_h: int) -> list[Image.Image | ImageFile.ImageFile]:
+    """Resizes and crops the image to the specified crop_w and crop_h if necessary.
+
+    Works with multi frame gif and webp images.
+
+    Args:
+      original_img(Image.Image): is the image instance created by pillow ( Image.open(filepath) )
+      crop_w(int): is the width in pixels for the image that will be resized and cropped
+      crop_h(int): is the height in pixels for the image that will be resized and cropped
+
+    returns:
+      Instance of an Image or list of frames which they are instances of an Image individually
+    """
+    img_w, img_h = (original_img.size[0], original_img.size[1])
+    n_frames = getattr(original_img, "n_frames", 1)
+
+    def transform_frame(frame: Image.Image) -> Image.Image | ImageFile.ImageFile:
+        """Resizes and crops the individual frame in the image."""
+        # resize the image to the specified height if crop_w is null in the recipe
+        if crop_w is None:
+            if crop_h == img_h:
+                return frame
+            new_w = floor(img_w * crop_h / img_h)
+            new_h = crop_h
+            return frame.resize((new_w, new_h), resample=Image.Resampling.LANCZOS)
+
+        # return the original image if crop size is equal to img size
+        if crop_w == img_w and crop_h == img_h:
+            return frame
+
+        # first resize to get most visible area of the image and then crop
+        w_diff = fabs(crop_w - img_w)
+        h_diff = fabs(crop_h - img_h)
+        enlarge_image = bool(crop_w > img_w or crop_h > img_h)
+        shrink_image = bool(crop_w < img_w or crop_h < img_h)
+
+        if enlarge_image is True:
+            new_w = floor(crop_h * img_w / img_h) if h_diff > w_diff else crop_w
+            new_h = floor(crop_w * img_h / img_w) if h_diff < w_diff else crop_h
+
+        if shrink_image is True:
+            new_w = crop_w if h_diff > w_diff else floor(crop_h * img_w / img_h)
+            new_h = crop_h if h_diff < w_diff else floor(crop_w * img_h / img_w)
+
+        left = (new_w - crop_w) // 2
+        right = left + crop_w
+        top = (new_h - crop_h) // 2
+        bottom = top + crop_h
+
+        return frame.resize((new_w, new_h), resample=Image.Resampling.LANCZOS).crop((left, top, right, bottom))
+
+    # single frame image
+    if n_frames == 1:
+        return [transform_frame(original_img)]
+    # in the case of a multiframe image
+    return [transform_frame(frame) for frame in ImageSequence.Iterator(original_img)]