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140 lines
4 KiB
Python
140 lines
4 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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# 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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# Import face recognition, takes some time
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timings.append(time.time())
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import face_recognition
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timings.append(time.time())
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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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# Fetch the max frame height
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max_height = int(config.get("video", "max_height"))
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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 the height and with of the image
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height, width = frame.shape[:2]
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# If the hight is too high
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if max_height < height:
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# Calculate the amount the image has to shrink
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scaling_factor = max_height / float(height)
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# Apply that factor to the frame
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frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
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# Save the new size for diagnostics
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scale_height, scale_width = frame.shape[:2]
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# Convert from BGR to RGB
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frame = frame[:, :, ::-1]
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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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def print_timing(label, offset):
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"""Helper function to print a timing from the list"""
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print(" " + label + ": " + str(round((timings[1 + offset] - timings[offset]) * 1000)) + "ms")
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print("Time spend")
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print_timing("Starting up", 0)
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print_timing("Importing face_recognition", 1)
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print_timing("Opening the camera", 2)
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print_timing("Searching for known face", 3)
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print("\nResolution")
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print(" Native: " + str(height) + "x" + str(width))
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print(" Used: " + str(scale_height) + "x" + str(scale_width))
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print("\nFrames searched: " + str(frames) + " (" + str(round(float(frames) / (timings[4] - timings[2]), 2)) + " fps)")
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print("Certainty of 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 time.time() - timings[3] > int(config.get("video", "timout")):
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stop(11)
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tries += 1
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