Fix typos

HISTORY.rst

@@ -49,15 +49,15 @@ History
 1.1.0 (2017-09-23)
 ------------------
 
-* Will use dlib's 5-point face pose estimator when possible for speed (instead of 68-point face pose esimator)
+* Will use dlib's 5-point face pose estimator when possible for speed (instead of 68-point face pose estimator)
 * dlib v19.7 is now the minimum required version
 * face_recognition_models v0.3.0 is now the minimum required version
 
 
 1.0.0 (2017-08-29)
 ------------------
 
-* Added support for dlib's CNN face detection model via model="cnn" parameter on face detecion call
+* Added support for dlib's CNN face detection model via model="cnn" parameter on face detection call
 * Added support for GPU batched face detections using dlib's CNN face detector model
 * Added find_faces_in_picture_cnn.py to examples
 * Added find_faces_in_batches.py to examples

README.md

@@ -260,7 +260,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image)
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py)
@@ -270,15 +270,15 @@ 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
 
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image, model="cnn")
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 See [this example](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py)

README.rst

@@ -260,7 +260,7 @@ Automatically find all the faces in an image
     image = face_recognition.load_image_file("my_picture.jpg")
     face_locations = face_recognition.face_locations(image)
 
-    # face_locations is now an array listing the co-ordinates of each face!
+    # face_locations is now an array listing the coordinates of each face!
 
 | See `this
   example <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py>`__
@@ -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
 
@@ -281,7 +281,7 @@ detection model.
     image = face_recognition.load_image_file("my_picture.jpg")
     face_locations = face_recognition.face_locations(image, model="cnn")
 
-    # face_locations is now an array listing the co-ordinates of each face!
+    # face_locations is now an array listing the coordinates of each face!
 
 | See `this
   example <https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py>`__

README_Japanese.md

@@ -226,7 +226,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image)
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 試す場合は[こちらのサンプルコード](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py)を参照してください。
@@ -241,7 +241,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image, model="cnn")
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 試す場合は[こちらのサンプルコード](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py)を参照してください。

README_Korean.md

@@ -218,7 +218,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image)
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 [이 예제](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py) 를 사용하여 테스트 해 보십시오.
@@ -233,7 +233,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image, model="cnn")
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 [이 예제](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py) 를 사용하여 테스트 해 보십시오.

README_Simplified_Chinese.md

@@ -246,7 +246,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image)
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 看 [案例：定位拜登的脸](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture.py)
@@ -263,7 +263,7 @@ import face_recognition
 image = face_recognition.load_image_file("my_picture.jpg")
 face_locations = face_recognition.face_locations(image, model="cnn")
 
-# face_locations is now an array listing the co-ordinates of each face!
+# face_locations is now an array listing the coordinates of each face!
 ```
 
 看 [案例：使用卷积神经网络深度学习模型定位拜登的脸](https://github.com/ageitgey/face_recognition/blob/master/examples/find_faces_in_picture_cnn.py)

examples/face_distance.py

@@ -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]
 

examples/face_recognition_knn.py

@@ -124,7 +124,7 @@ def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):
         raise Exception("Invalid image path: {}".format(X_img_path))
 
     if knn_clf is None and model_path is None:
-        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")
+        raise Exception("Must supply knn classifier either through knn_clf or model_path")
 
     # Load a trained KNN model (if one was passed in)
     if knn_clf is None:
@@ -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

examples/facerec_ipcamera_knn.py

@@ -126,7 +126,7 @@ def predict(X_frame, knn_clf=None, model_path=None, distance_threshold=0.5):
         For faces of unrecognized persons, the name 'unknown' will be returned.
     """
     if knn_clf is None and model_path is None:
-        raise Exception("Must supply knn classifier either thourgh knn_clf or model_path")
+        raise Exception("Must supply knn classifier either through knn_clf or model_path")
 
     # Load a trained KNN model (if one was passed in)
     if knn_clf is None:
@@ -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)