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- import face_recognition
- from PIL import Image, ImageDraw
- import numpy as np
- # This is an example of running face recognition on a single image
- # and drawing a box around each person that was identified.
- # Load a sample picture and learn how to recognize it.
- obama_image = face_recognition.load_image_file("obama.jpg")
- obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
- # Load a second sample picture and learn how to recognize it.
- biden_image = face_recognition.load_image_file("biden.jpg")
- biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
- # Create arrays of known face encodings and their names
- known_face_encodings = [
- obama_face_encoding,
- biden_face_encoding
- ]
- known_face_names = [
- "Barack Obama",
- "Joe Biden"
- ]
- # Load an image with an unknown face
- unknown_image = face_recognition.load_image_file("two_people.jpg")
- # Find all the faces and face encodings in the unknown image
- face_locations = face_recognition.face_locations(unknown_image)
- face_encodings = face_recognition.face_encodings(unknown_image, face_locations)
- # Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library
- # See http://pillow.readthedocs.io/ for more about PIL/Pillow
- pil_image = Image.fromarray(unknown_image)
- # Create a Pillow ImageDraw Draw instance to draw with
- draw = ImageDraw.Draw(pil_image)
- # Loop through each face found in the unknown image
- for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
- # See if the face is a match for the known face(s)
- matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
- name = "Unknown"
- # If a match was found in known_face_encodings, just use the first one.
- # if True in matches:
- # first_match_index = matches.index(True)
- # name = known_face_names[first_match_index]
- # Or instead, use the known face with the smallest distance to the new face
- face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
- best_match_index = np.argmin(face_distances)
- if matches[best_match_index]:
- name = known_face_names[best_match_index]
- # Draw a box around the face using the Pillow module
- draw.rectangle(((left, top), (right, bottom)), outline=(0, 0, 255))
- # Draw a label with a name below the face
- text_width, text_height = draw.textsize(name)
- draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(0, 0, 255), outline=(0, 0, 255))
- draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))
- # Remove the drawing library from memory as per the Pillow docs
- del draw
- # Display the resulting image
- pil_image.show()
- # You can also save a copy of the new image to disk if you want by uncommenting this line
- # pil_image.save("image_with_boxes.jpg")
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