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ความยากคือการลง library ตัวนี้. https://github.com/ageitgey/face_recognition อย่างตอนผมลงต้องลง Cmake dlib ก่อน แนะนำว่าให้ใช้ macos
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import face_recognition | |
import cv2 | |
import numpy as np | |
# เอา Code มาจาก https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py | |
video_capture = cv2.VideoCapture(0) | |
# Load a sample picture and learn how to recognize it. | |
patana_image = face_recognition.load_image_file("patana.png") #< ไฟล์ภาพหน้าตรง | |
patana_face_encoding = face_recognition.face_encodings(patana_image)[0] | |
# Load a sample picture and learn how to recognize it. | |
patana_mask_image = face_recognition.load_image_file("patana_mask.png") | |
patana_mask_face_encoding = face_recognition.face_encodings(patana_mask_image)[0] | |
# Load a second sample picture and learn how to recognize it. | |
kungfu_image = face_recognition.load_image_file("kungfu.png") #< หน้าคนอื่นๆ จริงๆถ้าทำเป็น list จับคู่กับชื่อก็ได้อยู่ | |
kungfu_face_encoding = face_recognition.face_encodings(kungfu_image)[0] | |
# Load a second sample picture and learn how to recognize it. | |
minya_image = face_recognition.load_image_file("minya.png") #< หน้าคนอื่นๆ จริงๆถ้าทำเป็น list จับคู่กับชื่อก็ได้อยู่ | |
minya_face_encoding = face_recognition.face_encodings(minya_image)[0] | |
# Create arrays of known face encodings and their names | |
known_face_encodings = [ | |
patana_face_encoding, | |
kungfu_face_encoding, | |
minya_face_encoding, | |
patana_mask_face_encoding | |
] | |
known_face_names = [ | |
"Patana", | |
"Kungfu", | |
"Minya", | |
"Patana Masked" | |
] #ชื่อที่จะให้โชว์ ตรงกับภาพที่ encode | |
# Initialize some variables | |
face_locations = [] | |
face_encodings = [] | |
face_names = [] | |
process_this_frame = True | |
while True: | |
# Grab a single frame of video | |
ret, frame = video_capture.read() | |
# Resize frame of video to 1/4 size for faster face recognition processing | |
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) | |
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) | |
rgb_small_frame = small_frame[:, :, ::-1] | |
# Only process every other frame of video to save time | |
if process_this_frame: | |
# Find all the faces and face encodings in the current frame of video | |
face_locations = face_recognition.face_locations(rgb_small_frame) | |
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations) | |
face_names = [] | |
for face_encoding in 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] | |
face_names.append(name) | |
process_this_frame = not process_this_frame | |
# Display the results | |
for (top, right, bottom, left), name in zip(face_locations, face_names): | |
# Scale back up face locations since the frame we detected in was scaled to 1/4 size | |
top *= 4 | |
right *= 4 | |
bottom *= 4 | |
left *= 4 | |
# Draw a box around the face | |
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) | |
# Draw a label with a name below the face | |
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED) | |
font = cv2.FONT_HERSHEY_DUPLEX | |
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1) | |
# Display the resulting image | |
cv2.imshow('Video', frame) | |
# Hit 'q' on the keyboard to quit! | |
if cv2.waitKey(1) & 0xFF == ord('q'): | |
break | |
# Release handle to the webcam | |
video_capture.release() | |
cv2.destroyAllWindows() |
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