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docs: Fix a few typos #1428

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docs: Fix a few typos #1428

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -175,7 +175,7 @@ your folder of known people.

#### `face_detection` command line tool

The `face_detection` command lets you find the location (pixel coordinatates)
The `face_detection` command lets you find the location (pixel coordinates)
of any faces in an image.

Just run the command `face_detection`, passing in a folder of images
Expand Down Expand Up @@ -270,7 +270,7 @@ You can also opt-in to a somewhat more accurate deep-learning-based face detecti

Note: GPU acceleration (via NVidia's CUDA library) is required for good
performance with this model. You'll also want to enable CUDA support
when compliling `dlib`.
when compiling `dlib`.

```python
import face_recognition
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2 changes: 1 addition & 1 deletion README.rst
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Expand Up @@ -272,7 +272,7 @@ detection model.
| Note: GPU acceleration (via nvidia's CUDA library) is required for
good
| performance with this model. You'll also want to enable CUDA support
| when compliling ``dlib``.
| when compiling ``dlib``.

.. code:: python

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2 changes: 1 addition & 1 deletion examples/face_distance.py
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Expand Up @@ -23,7 +23,7 @@
biden_face_encoding
]

# Load a test image and get encondings for it
# Load a test image and get encodings for it
image_to_test = face_recognition.load_image_file("obama2.jpg")
image_to_test_encoding = face_recognition.face_encodings(image_to_test)[0]

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2 changes: 1 addition & 1 deletion examples/face_recognition_knn.py
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Expand Up @@ -139,7 +139,7 @@ def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):
if len(X_face_locations) == 0:
return []

# Find encodings for faces in the test iamge
# Find encodings for faces in the test image
faces_encodings = face_recognition.face_encodings(X_img, known_face_locations=X_face_locations)

# Use the KNN model to find the best matches for the test face
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2 changes: 1 addition & 1 deletion examples/facerec_from_webcam.py
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Expand Up @@ -37,7 +37,7 @@
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]

# Find all the faces and face enqcodings in the frame of video
# Find all the faces and face encodings in the frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

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2 changes: 1 addition & 1 deletion examples/facerec_ipcamera_knn.py
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Expand Up @@ -156,7 +156,7 @@ def show_prediction_labels_on_image(frame, predictions):

:param frame: frame to show the predictions on
:param predictions: results of the predict function
:return opencv suited image to be fitting with cv2.imshow fucntion:
:return opencv suited image to be fitting with cv2.imshow function:
"""
pil_image = Image.fromarray(frame)
draw = ImageDraw.Draw(pil_image)
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