113 lines
3.1 KiB
Python
113 lines
3.1 KiB
Python
# Compare incomming video with known faces
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# Running in a local python instance to get around PATH issues
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# Import required modules
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import cv2
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import sys
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import os
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import json
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import time
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import math
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import configparser
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# Start timing
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timings = [time.time()]
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# Import face recognition, takes some time
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import face_recognition
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timings.append(time.time())
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# Read config from disk
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config = configparser.ConfigParser()
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config.read(os.path.dirname(os.path.abspath(__file__)) + "/config.ini")
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def stop(status):
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"""Stop the execution and close video stream"""
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video_capture.release()
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sys.exit(status)
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# Make sure we were given an username to tast against
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try:
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if not isinstance(sys.argv[1], str):
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sys.exit(1)
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except IndexError:
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sys.exit(1)
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# The username of the authenticating user
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user = sys.argv[1]
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# The model file contents
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models = []
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# Encoded face models
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encodings = []
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# Amount of frames already matched
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tries = 0
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# Try to load the face model from the models folder
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try:
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models = json.load(open(os.path.dirname(os.path.abspath(__file__)) + "/models/" + user + ".dat"))
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except FileNotFoundError:
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sys.exit(10)
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# Check if the file contains a model
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if len(models) < 1:
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sys.exit(10)
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# Put all models together into 1 array
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for model in models:
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encodings += model["data"]
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# Start video capture on the IR camera
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video_capture = cv2.VideoCapture(int(config.get("video", "device_id")))
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timings.append(time.time())
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# Start the read loop
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frames = 0
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while True:
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frames += 1
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# Grab a single frame of video
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# Don't remove ret, it doesn't work without it
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ret, frame = video_capture.read()
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# Get all faces from that frame as encodings
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face_encodings = face_recognition.face_encodings(frame)
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# Loop through each face
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for face_encoding in face_encodings:
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# Match this found face against a known face
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matches = face_recognition.face_distance(encodings, face_encoding)
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# Check if any match is certain enough to be the user we're looking for
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match_index = 0
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for match in matches:
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match_index += 1
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# Try to find a match that's confident enough
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if match * 10 < float(config.get("video", "certainty")) and match > 0:
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timings.append(time.time())
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# If set to true in the config, print debug text
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if config.get("debug", "end_report") == "true":
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print("DEBUG END REPORT\n")
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print("Time spend")
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print(" Importing face_recognition: " + str(round((timings[1] - timings[0]) * 1000)) + "ms")
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print(" Opening the camera: " + str(round((timings[2] - timings[1]) * 1000)) + "ms")
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print(" Searching for known face: " + str(round((timings[3] - timings[2]) * 1000)) + "ms\n")
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print("Frames searched: " + str(frames))
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print("Certainty winning frame: " + str(round(match * 10, 3)))
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exposures = ["long", "medium", "short"]
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model_id = math.floor(float(match_index) / 3)
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print("Winning model: " + str(model_id) + " (\"" + models[model_id]["label"] + "\") using " + exposures[match_index % 3] + " exposure\n")
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# End peacegully
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stop(0)
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# Stop if we've exceded the maximum retry count
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if tries > int(config.get("video", "frame_count")):
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stop(11)
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tries += 1
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