### Summary
- bump unstructured-inference to `0.7.35` which fixed syntax for
generated HTML tables
- update unit tests and ingest test fixtures to reflect changes in the
generated HTML tables
- cut a release for `0.14.6`
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
This PR aims to pass `kwargs` through `fast` strategy pipeline, which
was missing as part of the previous PR -
https://github.com/Unstructured-IO/unstructured/pull/3030.
I also did some code refactoring in this PR, so I recommend reviewing
this PR commit by commit.
### Summary
- pass `kwargs` through `fast` strategy pipeline, which will allow users
to specify additional params like `sort_mode`
- refactor: code reorganization
- cut a release for `0.14.0`
### Testing
CI should pass
Introduce `date_from_file_object` to `partition*` functions, by default
set to `False`.
If set to `True` and file is provided via `file` parameter, partition
will attempt to infer last modified date from `file`'s contents
otherwise last modified metadata will be set to `None`.
---------
Co-authored-by: Filip Knefel <filip@unstructured.io>
Co-authored-by: Ronny H <138828701+ron-unstructured@users.noreply.github.com>
.heic files are an image filetype we have not supported.
#### Testing
```
from unstructured.partition.image import partition_image
png_filename = "example-docs/DA-1p.png"
heic_filename = "example-docs/DA-1p.heic"
png_elements = partition_image(png_filename, strategy="hi_res")
heic_elements = partition_image(heic_filename, strategy="hi_res")
for i in range(len(heic_elements)):
print(heic_elements[i].text == png_elements[i].text)
```
---------
Co-authored-by: christinestraub <christinemstraub@gmail.com>
- there are multiple places setting the default `hi_res_model_name` in
both `unstructured` and `unstructured-inference`
- they lead to inconsistency and unexpected behaviors
- this fix removes a helper in `unstructured` that tries to set the
default hi_res layout detection model; instead we rely on the
`unstructured-inference` to provide that default when no explicit model
name is passed in
## test
```bash
UNSTRUCTURED_INCLUDE_DEBUG_METADATA=true ipython
```
```python
from unstructured.partition.auto import partition
# find a pdf file
elements = partition("foo.pdf", strategy="hi_res")
assert elements[0].metadata.detection_origin == "yolox"
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
### Summary
Adds support for bitmap images (`.bmp`) in both file detection and
partitioning. Bitmap images will be processed with `partition_image`
just like JPGs and PNGs.
### Testing
```python
from unstructured.file_utils.filetype import detect_filetype
from unstructured.partition.auto import partition
from PIL import Image
filename = "example-docs/layout-parser-paper-with-table.jpg"
bmp_filename = "~/tmp/ayout-parser-paper-with-table.bmp"
img = Image.open(filename)
img.save(bmp_filename)
detect_filetype(filename=bmp_filename) # Should be FileType.BMP
elements = partition(filename=bmp_filename)
```
Currently, we're using different kwarg names in partition() and
partition_pdf(), which has implications for the API since it goes
through partition().
### Summary
- rename `extract_element_types` -> `extract_image_block_types`
- rename `image_output_dir_path` to `extract_image_block_output_dir`
- rename `extract_to_payload` -> `extract_image_block_to_payload`
- rename `pdf_extract_images` -> `extract_images_in_pdf` in
`partition.auto`
- add unit tests to test element extraction for `pdf/image` via
`partition.auto`
### Testing
CI should pass.
Closes#2323.
### Summary
- update logic to return "hi_res" if either `extract_images_in_pdf` or
`extract_element_types` is set
- refactor: remove unused `file` parameter from
`determine_pdf_or_image_strategy()`
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
extract_element_types=["Image"],
extract_to_payload=True,
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print(image_elements)
```
Closes#2302.
### Summary
- add functionality to get a Base64 encoded string from a PIL image
- store base64 encoded image data in two metadata fields: `image_base64`
and `image_mime_type`
- update the "image element filter" logic to keep all image elements in
the output if a user specifies image extraction
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
extract_element_types=["Image", "Table"],
extract_to_payload=True,
)
```
or
```
from unstructured.partition.auto import partition
elements = partition(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
pdf_extract_element_types=["Image", "Table"],
pdf_extract_to_payload=True,
)
```
Closes#2160
Explicitly adds `hi_res_model_name` as kwarg to relevant functions and
notes that `model_name` is to be deprecated.
Testing:
```
from unstructured.partition.auto import partition
filename = "example-docs/DA-1p.pdf"
elements = partition(filename, strategy="hi_res", hi_res_model_name="yolox")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Steve Canny <stcanny@gmail.com>
Co-authored-by: Christine Straub <christinemstraub@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
### Summary
This PR is the second part of `pdfminer` refactor to move it from
`unstructured-inference` repo to `unstructured` repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/294. This
PR adds logic to merge the extracted layout with the inferred layout.
The updated workflow for the `hi_res` strategy:
* pass the document (as data/filename) to the `inference` repo to get
`inferred_layout` (DocumentLayout)
* pass the `inferred_layout` returned from the `inference` repo and the
document (as data/filename) to the `pdfminer_processing` module, which
first opens the document (create temp file/dir as needed), and splits
the document by pages
* if is_image is `True`, return the passed
inferred_layout(DocumentLayout)
* if is_image is `False`:
* get extracted_layout (TextRegions) from the passed
document(data/filename) by pdfminer
* merge `extracted_layout` (TextRegions) with the passed
`inferred_layout` (DocumentLayout)
* return the `inferred_layout `(DocumentLayout) with updated elements
(all merged LayoutElements) as merged_layout (DocumentLayout)
* pass merged_layout and the document (as data/filename) to the `OCR`
module, which first opens the document (create temp file/dir as needed),
and splits the document by pages (convert PDF pages to image pages for
PDF file)
### Note
This PR also fixes issue #2164 by using functionality similar to the one
implemented in the `fast` strategy workflow when extracting elements by
`pdfminer`.
### TODO
* image extraction refactor to move it from `unstructured-inference`
repo to `unstructured` repo
* improving natural reading order by applying the current default
`xycut` sorting to the elements extracted by `pdfminer`
### Summary
Closes#2011
`languages` was missing from the metadata when partitioning pdfs via
`hi_res` and `fast` strategies and missing from image partitions via
`hi_res`. This PR adds `languages` to the relevant function calls so it
is included in the resulting elements.
### Testing
On the main branch, `partition_image` will include `languages` when
`strategy='ocr_only'`, but not when `strategy='hi_res'`:
```
filename = "example-docs/english-and-korean.png"
from unstructured.partition.image import partition_image
elements = partition_image(filename, strategy="ocr_only", languages=['eng', 'kor'])
elements[0].metadata.languages
elements = partition_image(filename, strategy="hi_res", languages=['eng', 'kor'])
elements[0].metadata.languages
```
For `partition_pdf`, `'ocr_only'` will include `languages` in the
metadata, but `'fast'` and `'hi_res'` will not.
```
filename = "example-docs/korean-text-with-tables.pdf"
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(filename, strategy="ocr_only", languages=['kor'])
elements[0].metadata.languages
elements = partition_pdf(filename, strategy="fast", languages=['kor'])
elements[0].metadata.languages
elements = partition_pdf(filename, strategy="hi_res", languages=['kor'])
elements[0].metadata.languages
```
On this branch, `languages` is included in the metadata regardless of
strategy
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Coniferish <Coniferish@users.noreply.github.com>
### Summary
- add constants for element type
- replace the `TYPE_TO_TEXT_ELEMENT_MAP` dictionary using the
`ElementType` constants
- replace element type strings using the constants
### Testing
CI should pass.
### Summary
Update `ocr_only` strategy in `partition_pdf()`. This PR adds the
functionality to get accurate coordinate data when partitioning PDFs and
Images with the `ocr_only` strategy.
- Add functionality to perform OCR region grouping based on the OCR text
taken from `pytesseract.image_to_string()`
- Add functionality to get layout elements from OCR regions (ocr_layout)
for both `tesseract` and `paddle`
- Add functionality to determine the `source` of merged text regions
when merging text regions in `merge_text_regions()`
- Merge multiple test functions related to "ocr_only" strategy into
`test_partition_pdf_with_ocr_only_strategy()`
- This PR also fixes [issue
#1792](https://github.com/Unstructured-IO/unstructured/issues/1792)
### Evaluation
```
# Image
PYTHONPATH=. python examples/custom-layout-order/evaluate_natural_reading_order.py example-docs/double-column-A.jpg ocr_only xy-cut image
# PDF
PYTHONPATH=. python examples/custom-layout-order/evaluate_natural_reading_order.py example-docs/multi-column-2p.pdf ocr_only xy-cut pdf
```
### Test
- **Before update**
All elements have the same coordinate data
- **After update**
All elements have accurate coordinate data
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
This PR introduces `clean_pdfminer_inner_elements` , which deletes
pdfminer elements inside other detection origins such as YoloX or
detectron.
This function returns the clean document.
Also, the ingest-test fixtures were updated to reflect the new standard
output.
The best way to check that this function is working properly is check
the new test `test_clean_pdfminer_inner_elements` in
`test_unstructured/partition/utils/test_processing_elements.py`
---------
Co-authored-by: Roman Isecke <roman@unstructured.io>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
Co-authored-by: Roman Isecke <136338424+rbiseck3@users.noreply.github.com>
Closes
[#1859](https://github.com/Unstructured-IO/unstructured/issues/1859).
* **Fixes elements partitioned from an image file missing certain
metadata** Metadata for image files, like file type, was being handled
differently from other file types. This caused a bug where other
metadata, like the file name, was being missed. This change brought
metadata handling for image files to be more in line with the handling
for other file types so that file name and other metadata fields are
being captured.
Additionally:
* Added test to verify filename is being captured in metadata
* Cleaned up `CHANGELOG.md` formatting
#### Testing:
The following produces output `None` on `main`, but outputs the filename
`layout-parser-paper-fast.jpg` on this branch:
```python
from unstructured.partition.auto import partition
elements = partition("example-docs/layout-parser-paper-fast.jpg")
print(elements[0].metadata.filename)
```
### Summary
A follow up ticket on
https://github.com/Unstructured-IO/unstructured/pull/1801, I forgot to
remove the lines that pass extract_tables to inference, and noted the
table regression if we only do one OCR for entire doc
**Tech details:**
* stop passing `extract_tables` parameter to inference
* added table extraction ingest test for image, which was skipped
before, and the "text_as_html" field contains the OCR output from the
table OCR refactor PR
* replaced `assert_called_once_with` with `call_args` so that the unit
tests don't need to test additional parameters
* added `error_margin` as ENV when comparing bounding boxes
of`ocr_region` with `table_element`
* added more tests for tables and noted the table regression in test for
partition pdf
### Test
* for stop passing `extract_tables` parameter to inference, run test
`test_partition_pdf_hi_res_ocr_mode_with_table_extraction` before this
branch and you will see warning like `Table OCR from get_tokens method
will be deprecated....`, which means it called the table OCR in
inference repo. This branch removed the warning.
PR to support schema changes introduced from [PR
232](https://github.com/Unstructured-IO/unstructured-inference/pull/232)
in `unstructured-inference`.
Specifically what needs to be supported is:
* Change to the way `LayoutElement` from `unstructured-inference` is
structured, specifically that this class is no longer a subclass of
`Rectangle`, and instead `LayoutElement` has a `bbox` property that
captures the location information and a `from_coords` method that allows
construction of a `LayoutElement` directly from coordinates.
* Removal of `LocationlessLayoutElement` since chipper now exports
bounding boxes, and if we need to support elements without bounding
boxes, we can make the `bbox` property mentioned above optional.
* Getting hierarchy data directly from the inference elements rather
than in post-processing
* Don't try to reorder elements received from chipper v2, as they should
already be ordered.
#### Testing:
The following demonstrates that the new version of chipper is inferring
hierarchy.
```python
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf("example-docs/layout-parser-paper-fast.pdf", strategy="hi_res", model_name="chipper")
children = [el for el in elements if el.metadata.parent_id is not None]
print(children)
```
Also verify that running the traditional `hi_res` gives different
results:
```python
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf("example-docs/layout-parser-paper-fast.pdf", strategy="hi_res")
```
---------
Co-authored-by: Sebastian Laverde Alfonso <lavmlk20201@gmail.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
Each partitioner has a test like `test_partition_x_with_json()`. What
these do is serialize the elements produced by the partitioner to JSON,
then read them back in from JSON and compare the before and after
elements.
Because our element equality (`Element.__eq__()`) is shallow, this
doesn't tell us a lot, but if we take it one more step, like
`List[Element] -> JSON -> List[Element] -> JSON` and then compare the
JSON, it gives us some confidence that the serialized elements can be
"re-hydrated" without losing any information.
This actually showed up a few problems, all in the
serialization/deserialization (serde) code that all elements share.
