Commit f4e402f8 authored by Hailu, Dawit Abiy's avatar Hailu, Dawit Abiy
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parent 83689d8f
import cv2
import mediapipe as mp
import os
import time
import deepface
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
# For static images:
# IMAGE_FILES = []
# with mp_face_detection.FaceDetection(
# model_selection=1, min_detection_confidence=0.5) as face_detection:
# for idx, file in enumerate(IMAGE_FILES):
# image = cv2.imread(file)
# # Convert the BGR image to RGB and process it with MediaPipe Face Detection.
# results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# # Draw face detections of each face.
# if not results.detections:
# continue
# annotated_image = image.copy()
# for detection in results.detections:
# print('Nose tip:')
# print(mp_face_detection.get_key_point(
# detection, mp_face_detection.FaceKeyPoint.NOSE_TIP))
# mp_drawing.draw_detection(annotated_image, detection)
# cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
# For webcam input:
# findcounter = 0
# notfindcounter = 0
# totalcounter = 0
# filename = 'video2.mp4'
# frames_per_second = 24.0
# res = '720p'
# # Set resolution for the video capture
# # Function adapted from https://kirr.co/0l6qmh
# def change_res(cap, width, height):
# cap.set(3, width)
# cap.set(4, height)
# # Standard Video Dimensions Sizes
# STD_DIMENSIONS = {
# "480p": (640, 480),
# "720p": (1280, 720),
# "1080p": (1920, 1080),
# "4k": (3840, 2160),
# }
# # grab resolution dimensions and set video capture to it.
# def get_dims(cap, res='720'):
# width, height = STD_DIMENSIONS["480p"]
# if res in STD_DIMENSIONS:
# width,height = STD_DIMENSIONS[res]
# ## change the current caputre device
# ## to the resulting resolution
# change_res(cap, width, height)
# return width, height
# # Video Encoding, might require additional installs
# # Types of Codes: http://www.fourcc.org/codecs.php
# VIDEO_TYPE = {
# 'avi': cv2.VideoWriter_fourcc(*'XVID'),
# #'mp4': cv2.VideoWriter_fourcc(*'H264'),
# 'mp4': cv2.VideoWriter_fourcc(*'XVID'),
# }
# def get_video_type(filename):
# filename, ext = os.path.splitext(filename)
# if ext in VIDEO_TYPE:
# return VIDEO_TYPE[ext]
# return VIDEO_TYPE['avi']
# # FPS = 1/30
# # FPS_MS = int(FPS * 1000)
# num_frames = 120
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture("/home/beyondem/Downloads/VID_20220225_170957.mp4")
# out = cv2.VideoWriter(filename, get_video_type(filename), 30, get_dims(cap, res))
# cap = cv2.VideoCapture(1)
fd = mp_face_detection.FaceDetection(min_detection_confidence=0.4, model_selection=1)
with fd as face_detection:
while cap.isOpened():
start = time.time()
success, image = cap.read()
# time.sleep(0.5)
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
continue
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
results = face_detection.process(image)
end = time.time()
# Time elapsed
seconds = end - start
fps = 1/seconds
# Draw the face detection annotations on the image.
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.detections:
# print(len(results.detections))
for detection in results.detections:
mp_drawing.draw_detection(image, detection)
image = cv2.flip(image, 1)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image, str(fps) ,(100,351), font, 1,(110,110,255),2,cv2.LINE_AA)
# Flip the image horizontally for a selfie-view display.
cv2.imshow('MediaPipe Face Detection', image)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
# out.release()
cv2.destroyAllWindows()
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