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| 1 | import face_recognition | |
| 2 | import cv2 | |
| 3 | import os | |
| 4 | from PIL import Image | |
| 5 | import numpy as np | |
| 6 | ||
| 7 | ||
| 8 | # Tune this this to memory. | |
| 9 | BATCH_FRAMES = 25 | |
| 10 | ||
| 11 | def process_video(vid): | |
| 12 | print(f"Processing: {vid}")
| |
| 13 | basename, _ = os.path.splitext(os.path.basename(vid)) | |
| 14 | video_capture = cv2.VideoCapture(vid) | |
| 15 | faces = 0 | |
| 16 | frames = [] | |
| 17 | while video_capture.isOpened(): | |
| 18 | # Grab a single frame of video | |
| 19 | ret, frame_cv = video_capture.read() | |
| 20 | ||
| 21 | # Bail out when the video file ends | |
| 22 | if not ret: | |
| 23 | break | |
| 24 | ||
| 25 | frame = cv2.cvtColor(frame_cv, cv2.COLOR_BGR2RGB) | |
| 26 | frames.append(frame) | |
| 27 | print(".", end="", flush=""),
| |
| 28 | # Tune this to GPU Memory Size. | |
| 29 | if len(frames) == BATCH_FRAMES: | |
| 30 | print("#", end="", flush="")
| |
| 31 | batch_of_face_locations = face_recognition.batch_face_locations( | |
| 32 | frames, number_of_times_to_upsample=0 | |
| 33 | ) | |
| 34 | ||
| 35 | for idx,face_locations in enumerate(batch_of_face_locations): | |
| 36 | if len(face_locations)==0: | |
| 37 | continue | |
| 38 | ||
| 39 | face_encodings = face_recognition.face_encodings( | |
| 40 | face_image=frames[idx], | |
| 41 | known_face_locations=face_locations | |
| 42 | ) | |
| 43 | for face_encoding, face_location in zip(face_encodings, face_locations): | |
| 44 | print("$", end="", flush=True)
| |
| 45 | top, right, bottom, left = face_location | |
| 46 | ||
| 47 | buffer = 0.5 | |
| 48 | max_bottom, max_right, _ = frame.shape | |
| 49 | width = right - left | |
| 50 | height = bottom - top | |
| 51 | top2 = int(max([0, top - height * buffer])) | |
| 52 | left2 = int(max([0, left - width * buffer])) | |
| 53 | ||
| 54 | bottom2 = int(min([max_bottom, bottom + height * buffer])) | |
| 55 | right2 = int(min([max_right, right + width * buffer])) | |
| 56 | ||
| 57 | # You can access the actual face itself like this: | |
| 58 | face_image = frame[top2:bottom2, left2:right2] | |
| 59 | pil_image = Image.fromarray(face_image) | |
| 60 | ||
| 61 | pil_image.save(f"faces/{basename}-{faces:08d}.jpg")
| |
| 62 | ||
| 63 | with open(f"faces/{basename}-{faces:08d}.np", "wb") as b:
| |
| 64 | np.save(b, face_encoding, allow_pickle=False, fix_imports=False) | |
| 65 | ||
| 66 | faces += 1 | |
| 67 | print("", end="\n", flush=True)
| |
| 68 | frames = [] | |
| 69 | ||
| 70 | print("#", end="", flush="")
| |
| 71 | batch_of_face_locations = face_recognition.batch_face_locations( | |
| 72 | frames, number_of_times_to_upsample=0 | |
| 73 | ) | |
| 74 | ||
| 75 | for idx,face_locations in enumerate(batch_of_face_locations): | |
| 76 | if len(face_locations)==0: | |
| 77 | continue | |
| 78 | ||
| 79 | face_encodings = face_recognition.face_encodings( | |
| 80 | face_image=frames[idx], | |
| 81 | known_face_locations=face_locations | |
| 82 | ) | |
| 83 | for face_encoding, face_location in zip(face_encodings, face_locations): | |
| 84 | print("$", end="", flush=True)
| |
| 85 | top, right, bottom, left = face_location | |
| 86 | ||
| 87 | buffer = 0.1 | |
| 88 | max_bottom, max_right, _ = frame.shape | |
| 89 | width = right - left | |
| 90 | height = bottom - top | |
| 91 | top2 = int(max([0, top - height * buffer])) | |
| 92 | left2 = int(max([0, left - width * buffer])) | |
| 93 | ||
| 94 | bottom2 = int(min([max_bottom, bottom + height * buffer])) | |
| 95 | right2 = int(min([max_right, right + width * buffer])) | |
| 96 | ||
| 97 | # You can access the actual face itself like this: | |
| 98 | face_image = frame[top2:bottom2, left2:right2] | |
| 99 | pil_image = Image.fromarray(face_image) | |
| 100 | ||
| 101 | pil_image.save(f"faces/{basename}-{faces:08d}.jpg")
| |
| 102 | ||
| 103 | with open(f"faces/{basename}-{faces:08d}.np", "wb") as b:
| |
| 104 | np.save(b, face_encoding, allow_pickle=False, fix_imports=False) | |
| 105 | faces += 1 | |
| 106 | ||
| 107 | # Open video file | |
| 108 | import glob | |
| 109 | ||
| 110 | vids = glob.glob("*.mp4")
| |
| 111 | for vid in vids: | |
| 112 | process_video(vid) | |
| 113 |
