Fix/reduce memory usage (#3629)
This PR fixes the high memory usage when computing intersection areas.
- it now converts the coordinates into half precision floating point
numbers instead of double
- removes some intermediate variables to free up memory usage
## test
Using a memory profiler like `memory_profiler` in `ipython`:
```ipython
## cell 1
from unstructured.partition.pdf_image.pdfminer_processing import areas_of_boxes_and_intersection_area
import numpy as np
%load_ext memory_profiler
## cell 2
%%memit
coords = np.random.rand(40000).reshape((10000,4)).astype(np.float16)
## cell 3
%%memit
inter_area, boxa_area, boxb_area = areas_of_boxes_and_intersection_area(coords, coords)
```
The peak memory and incremental memory from cell 3 should be close to
```
peak memory: 730.55 MiB, increment: 573.22 MiB
```
On main branch the `coords` is double precision and running the same
code with
```
coords = np.random.rand(40000).reshape((10000,4)).astype(np.float64)
```
would result in peak memory usage more than 4GiB
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
