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video.py
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video.py
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#!/usr/bin/env python
from sys import argv
import cv2
if len(argv) == 1:
print(f"Usage: {argv[0]} <video file path>")
exit(1)
# VIDEO_PATH = "C:\\Users\\Naman Tamrakar\\Videos\\28.01.2022_15.36.07_REC.mp4"
VIDEO_PATH = argv[1]
from functions import *
rotate = False
skip_frames = 20 # i.e. at 30 fps, this advances one second
video_capture = cv2.VideoCapture(VIDEO_PATH)
video_frames = video_capture.get(cv2.CAP_PROP_FRAME_COUNT)
video_width = video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)
video_height = video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
video_fps = video_capture.get(cv2.CAP_PROP_FPS)
print(f"Total frames: {video_frames}, height: {video_height}, width: {video_width}, "
f"FPS: {video_fps}, Skipping frames: {skip_frames}")
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
count = 0
while True:
current_frame_count = video_capture.get(cv2.CAP_PROP_POS_FRAMES)
print(f"Processing frame {current_frame_count}...")
# Grab a single frame of video
success, frame = video_capture.read()
if rotate:
frame = cv2.rotate(frame, cv2.ROTATE_180)
# if frame empty exit
if not success or current_frame_count >= video_frames:
break
times = 0.8
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=times, fy=times)
# small_frame = frame.copy()
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# print(rgb_small_frame.shape)
# Find all the faces and face encodings in the current frame of video
face_locations = get_face_locations(rgb_small_frame)
face_encodings = get_face_encodings(rgb_small_frame, face_locations)
# print(len(face_encodings))
face_names = []
for face_encoding in face_encodings:
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
# if matches[best_match_index]:
if face_distances[best_match_index] <= threshold:
name = known_face_labels[best_match_index]
else:
name = "Unknown"
face_names.append(name)
# 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
if times != 1:
top = int(top * (1/times))
right = int(right * (1/times))
bottom = int(bottom * (1/times))
left = int(left * (1/times))
# 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), (255, 0, 0), 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
if skip_frames:
count += skip_frames # i.e. at 30 fps, this advances one second
video_capture.set(cv2.CAP_PROP_POS_FRAMES, count)
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()