feat(phase1): implement OpenCV image processing pipeline

- Create ImageProcessor service with EXIF orientation detection
- Implement smart cropping via OpenCV contour detection (10% padding)
- Add text orientation detection using Hough line transform
- Resize and compress images to 1200px with 85% JPEG quality
- Generate 200px square thumbnails with center crop
- Fallback to Pillow if OpenCV fails
- Comprehensive test suite: 28 tests all passing
- File size validation (reject >10MB)
- Graceful error handling for corrupted/invalid images
- Update requirements.txt with opencv-python, piexif, python-magic

venv/lib/python3.12/site-packages/cv2/mat_wrapper/__init__.py

@@ -0,0 +1,40 @@
+__all__ = []
+
+import numpy as np
+import cv2 as cv
+from typing import TYPE_CHECKING, Any
+
+# Same as cv2.typing.NumPyArrayNumeric, but avoids circular dependencies
+if TYPE_CHECKING:
+    _NumPyArrayNumeric = np.ndarray[Any, np.dtype[np.integer[Any] | np.floating[Any]]]
+else:
+    _NumPyArrayNumeric = np.ndarray
+
+# NumPy documentation: https://numpy.org/doc/stable/user/basics.subclassing.html
+
+
+class Mat(_NumPyArrayNumeric):
+    '''
+    cv.Mat wrapper for numpy array.
+
+    Stores extra metadata information how to interpret and process of numpy array for underlying C++ code.
+    '''
+
+    def __new__(cls, arr, **kwargs):
+        obj = arr.view(Mat)
+        return obj
+
+    def __init__(self, arr, **kwargs):
+        self.wrap_channels = kwargs.pop('wrap_channels', getattr(arr, 'wrap_channels', False))
+        if len(kwargs) > 0:
+            raise TypeError('Unknown parameters: {}'.format(repr(kwargs)))
+
+    def __array_finalize__(self, obj):
+        if obj is None:
+            return
+        self.wrap_channels = getattr(obj, 'wrap_channels', None)
+
+
+Mat.__module__ = cv.__name__
+cv.Mat = Mat
+cv._registerMatType(Mat)