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Merge pull request #8 from Boltgolt/dev

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A wide range of fixes and improvements
This commit is contained in:
boltgolt 2018-02-20 23:14:58 +01:00 committed by GitHub
commit 10aae8266c
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9 changed files with 298 additions and 91 deletions
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2
README.md
View file

@ -12,7 +12,7 @@ Run the installer by pasting (`ctrl+shift+V`) the following command into the ter
wget -O /tmp/howdy_install.py https://raw.githubusercontent.com/Boltgolt/howdy/master/installer.py && sudo python3 /tmp/howdy_install.py
```
This will guide you through the installation. When that's done run `howdy USER add` and replace `USER` with your username to add a face model.
This will guide you through the installation. When that's done run `sudo howdy USER add` and replace `USER` with your username to add a face model.
If nothing went wrong we should be able to run sudo by just showing your face. Open a new terminal and run `sudo -i` to see it in action.

60
cli.py
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@ -1,41 +1,53 @@
#!/usr/bin/env python3
# CLI directly called by running the howdy command
# Import required modules
import sys
import os
# Check if the minimum of 3 arugemnts has been met and print help otherwise
if (len(sys.argv) < 3):
# Check if if a command has been given and print help otherwise
if (len(sys.argv) < 2):
print("Howdy IR face recognition help")
import cli.help
sys.exit()
# The command given
cmd = sys.argv[2]
cmd = sys.argv[1]
# Requre sudo for comamnds that need root rights to read the model files
if cmd in ["list", "add", "remove", "clear"] and os.getenv("SUDO_USER") is None:
print("Please run this command with sudo")
sys.exit()
# Call the right files for the given command
if cmd == "list":
import cli.list
elif cmd == "help":
# Call the right files for commands that don't need root
if cmd == "help":
print("Howdy IR face recognition")
import cli.help
elif cmd == "add":
import cli.add
elif cmd == "remove":
import cli.remove
elif cmd == "clear":
import cli.clear
elif cmd == "test":
import cli.test
else:
# If the comand is invalid, check if the user hasn't swapped the username and command
if sys.argv[1] in ["list", "add", "remove", "clear", "help"]:
print("Usage: howdy <user> <command>")
else:
print('Unknown command "' + cmd + '"')
# Check if the minimum of 3 arugemnts has been met and print help otherwise
if (len(sys.argv) < 3):
print("Howdy IR face recognition help")
import cli.help
sys.exit()
# Requre sudo for comamnds that need root rights to read the model files
if os.getenv("SUDO_USER") is None:
print("Please run this command with sudo")
sys.exit()
# Frome here on we require the second argument to be the username,
# switching the command to the 3rd
cmd = sys.argv[2]
if cmd == "list":
import cli.list
elif cmd == "add":
import cli.add
elif cmd == "remove":
import cli.remove
elif cmd == "clear":
import cli.clear
else:
# If the comand is invalid, check if the user hasn't swapped the username and command
if sys.argv[1] in ["list", "add", "remove", "clear"]:
print("Usage: howdy <user> <command>")
else:
print('Unknown command "' + cmd + '"')
import cli.help

1
cli/__init__.py
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@ -0,0 +1 @@
# Marks this folder as importable

104
cli/add.py
View file

@ -6,9 +6,9 @@ import time
import os
import sys
import json
import cv2
import configparser
# Try to import face_recognition and give a nice error if we can't
# Add should be the first point where import issues show up
try:
@ -27,50 +27,8 @@ path = os.path.dirname(os.path.abspath(__file__))
config = configparser.ConfigParser()
config.read(path + "/../config.ini")
def captureFrame(delay):
"""Capture and encode 1 frame of video"""
global insert_model
# Call fswebcam to save a frame to /tmp with a set delay
exit_code = subprocess.call(["fswebcam", "-S", str(delay), "--no-banner", "-d", "/dev/video" + str(config.get("video", "device_id")), tmp_file])
# Check if fswebcam exited normally
if (exit_code != 0):
print("Webcam frame capture failed!")
print("Please make sure fswebcam is installed on this system")
sys.exit()
# Try to load the image from disk
try:
ref = face_recognition.load_image_file(tmp_file)
except FileNotFoundError:
print("No webcam frame captured, check if /dev/video" + str(config.get("video", "device_id")) + " is the right webcam")
sys.exit()
# Make a face encoding from the loaded image
enc = face_recognition.face_encodings(ref)
# If 0 faces are detected we can't continue
if len(enc) == 0:
print("No face detected, aborting")
sys.exit()
# If more than 1 faces are detected we can't know wich one belongs to the user
if len(enc) > 1:
print("Multiple faces detected, aborting")
sys.exit()
clean_enc = []
# Copy the values into a clean array so we can export it as JSON later on
for point in enc[0]:
clean_enc.append(point)
insert_model["data"].append(clean_enc)
# The current user
user = sys.argv[1]
# The name of the tmp frame file to user
tmp_file = "/tmp/howdy_" + user + ".jpg"
# The permanent file to store the encoded model in
enc_file = path + "/../models/" + user + ".dat"
# Known encodings
@ -87,6 +45,11 @@ try:
except FileNotFoundError:
encodings = []
# Print a warning if too many encodings are being added
if len(encodings) > 2:
print("WARNING: Every additional model slows down the face recognition engine")
print("Press ctrl+C to cancel")
print("Adding face model for the user account " + user)
# Set the default label
@ -111,15 +74,53 @@ insert_model = {
"data": []
}
print("\nPlease look straight into the camera for 5 seconds")
# Open the camera
video_capture = cv2.VideoCapture(int(config.get("video", "device_id")))
video_capture.read()
print("\nPlease look straight into the camera")
# Give the user time to read
time.sleep(2)
# Capture with 3 different delays to simulate different camera exposures
for delay in [30, 6, 0]:
time.sleep(.3)
captureFrame(delay)
# Will contain found face encodings
enc = []
# Count the amount or read frames
frames = 0
# Loop through frames till we hit a timeout
while frames < 60:
frames += 1
# Grab a single frame of video
# Don't remove ret, it doesn't work without it
ret, frame = video_capture.read()
# Get the encodings in the frame
enc = face_recognition.face_encodings(frame)
# If we've found at least one, we can continue
if len(enc) > 0:
break
# If 0 faces are detected we can't continue
if len(enc) == 0:
print("No face detected, aborting")
sys.exit()
# If more than 1 faces are detected we can't know wich one belongs to the user
if len(enc) > 1:
print("Multiple faces detected, aborting")
sys.exit()
# Totally clean array that can be exported as JSON
clean_enc = []
# Copy the values into a clean array so we can export it as JSON later on
for point in enc[0]:
clean_enc.append(point)
insert_model["data"].append(clean_enc)
# Insert full object into the list
encodings.append(insert_model)
@ -128,7 +129,6 @@ encodings.append(insert_model)
with open(enc_file, "w") as datafile:
json.dump(encodings, datafile)
# Remove any left over temp files
os.remove(tmp_file)
print("Done.")
# Give let the user know how it went
print("Scan complete")
print("\nAdded a new model to " + user)

1
cli/help.py
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@ -10,6 +10,7 @@ Commands:
add Add a new face model for the current user
remove [id] Remove a specific model
clear Remove all face models for the current user
test Test the camera and recognition methods
For support please visit
https://github.com/Boltgolt/howdy\

161
cli/test.py Normal file
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@ -0,0 +1,161 @@
# Show a windows with the video stream and testing information
# Import required modules
import face_recognition
import cv2
import configparser
import os
import sys
import json
import numpy
import time
# Get the absolute path to the current file
path = os.path.dirname(os.path.abspath(__file__))
# Read config from disk
config = configparser.ConfigParser()
config.read(path + "/../config.ini")
# Start capturing from the configured webcam
video_capture = cv2.VideoCapture(int(config.get("video", "device_id")))
# Let the user know what's up
print("""
Opening a window with a test feed
Press ctrl+C in this terminal to quit
Click on the image to enable or disable slow mode
""")
def mouse(event, x, y, flags, param):
"""Handle mouse events"""
global slow_mode
# Toggle slowmode on click
if event == cv2.EVENT_LBUTTONDOWN:
slow_mode = not slow_mode
# Open the window and attach a a mouse listener
cv2.namedWindow("Howdy Test")
cv2.setMouseCallback("Howdy Test", mouse)
# Enable a delay in the loop
slow_mode = False
# Count all frames ever
total_frames = 0
# Count all frames per second
sec_frames = 0
# Last secands FPS
fps = 0
# The current second we're counting
sec = int(time.time())
# Wrap everything in an keyboard interupt handler
try:
while True:
# Inclement the frames
total_frames += 1
sec_frames += 1
# Id we've entered a new second
if sec != int(time.time()):
# Set the last seconds FPS
fps = sec_frames
# Set the new second and reset the counter
sec = int(time.time())
sec_frames = 0
# Grab a single frame of video
ret, frame = (video_capture.read())
# Make a frame to put overlays in
overlay = frame.copy()
# Fetch the frame height and width
height, width = frame.shape[:2]
# Create a histogram of the image with 8 values
hist = cv2.calcHist([frame], [0], None, [8], [0, 256])
# All values combined for percentage calculation
hist_total = int(sum(hist)[0])
# Fill with the overal containing percentage
hist_perc = []
# Loop though all values to calculate a pensentage and add it to the overlay
for index, value in enumerate(hist):
value_perc = float(value[0]) / hist_total * 100
hist_perc.append(value_perc)
# Top left pont, 10px margins
p1 = (20 + (10 * index), 10)
# Bottom right point makes the bar 10px thick, with an height of half the percentage
p2 = (10 + (10 * index), int(value_perc / 2 + 10))
# Draw the bar in green
cv2.rectangle(overlay, p1, p2, (0, 200, 0), thickness=cv2.FILLED)
# Draw a stripe indicating the dark threshold
cv2.rectangle(overlay, (8, 35), (20, 36), (255, 0, 0), thickness=cv2.FILLED)
def print_text(line_number, text):
"""Print the status text by line number"""
cv2.putText(overlay, text, (10, height - 10 - (10 * line_number)), cv2.FONT_HERSHEY_SIMPLEX, .3, (0, 255, 0), 0, cv2.LINE_AA)
# Print the statis in the bottom left
print_text(0, "RESOLUTION: " + str(height) + "x" + str(width))
print_text(1, "FPS: " + str(fps))
print_text(2, "FRAMES: " + str(total_frames))
# Show that slow mode is on, if it's on
if slow_mode:
cv2.putText(overlay, "SLOW MODE", (width - 66, height - 10), cv2.FONT_HERSHEY_SIMPLEX, .3, (0, 0, 255), 0, cv2.LINE_AA)
# Ignore dark frames
if hist_perc[0] > 50:
# Show that this is an ignored frame in the top right
cv2.putText(overlay, "DARK FRAME", (width - 68, 16), cv2.FONT_HERSHEY_SIMPLEX, .3, (0, 0, 255), 0, cv2.LINE_AA)
else:
# SHow that this is an active frame
cv2.putText(overlay, "SCAN FRAME", (width - 68, 16), cv2.FONT_HERSHEY_SIMPLEX, .3, (0, 255, 0), 0, cv2.LINE_AA)
# Get the locations of all faces and their locations
face_locations = face_recognition.face_locations(frame)
# Loop though all faces and paint a circle around them
for loc in face_locations:
# Get the center X and Y from the rectangular points
x = int((loc[1] - loc[3]) / 2) + loc[3]
y = int((loc[2] - loc[0]) / 2) + loc[0]
# Get the raduis from the with of the square
r = (loc[1] - loc[3]) / 2
# Add 20% padding
r = int(r + (r * 0.2))
# Draw the Circle in green
cv2.circle(overlay, (x, y), r, (0, 0, 230), 2)
# Add the overlay to the frame with some transparency
alpha = 0.65
cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
# Show the image in a window
cv2.imshow("Howdy Test", frame)
# Quit on any keypress
if cv2.waitKey(1) != -1:
raise KeyboardInterrupt()
# Delay the frame if slowmode is on
if slow_mode:
time.sleep(.55)
# On ctrl+C
except KeyboardInterrupt:
# Let the user know we're stopping
print("\nClosing window")
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()

52
compare.py
View file

@ -1,18 +1,20 @@
# Compare incomming video with known faces
# Running in a local python instance to get around PATH issues
# Import time so we can start timing asap
import time
# Start timing
timings = [time.time()]
# Import required modules
import cv2
import sys
import os
import json
import time
import math
import configparser
# Start timing
timings = [time.time()]
# Read config from disk
config = configparser.ConfigParser()
config.read(os.path.dirname(os.path.abspath(__file__)) + "/config.ini")
@ -37,6 +39,8 @@ models = []
encodings = []
# Amount of frames already matched
tries = 0
# Amount of ingnored dark frames
dark_tries = 0
# Try to load the face model from the models folder
try:
@ -52,13 +56,21 @@ if len(models) < 1:
for model in models:
encodings += model["data"]
# Import face recognition, takes some time
timings.append(time.time())
import face_recognition
# Add the time needed to start the script
timings.append(time.time())
# Start video capture on the IR camera
video_capture = cv2.VideoCapture(int(config.get("video", "device_id")))
# Capture a single frame so the camera becomes active
# This will let the camera adjust its light levels while we're importing for faster scanning
video_capture.read()
# Note the time it took to open the camera
timings.append(time.time())
# Import face recognition, takes some time
import face_recognition
timings.append(time.time())
# Fetch the max frame height
@ -67,12 +79,23 @@ max_height = int(config.get("video", "max_height"))
# Start the read loop
frames = 0
while True:
# Increment the frame count every loop
frames += 1
# Grab a single frame of video
# Don't remove ret, it doesn't work without it
ret, frame = video_capture.read()
# Create a histogram of the image with 8 values
hist = cv2.calcHist([frame], [0], None, [8], [0, 256])
# All values combined for percentage calculation
hist_total = int(sum(hist)[0])
# Scrip the frame if it exceeds the threshold
if float(hist[0]) / hist_total * 100 > float(config.get("video", "dark_threshold")):
dark_tries += 1
continue
# Get the height and with of the image
height, width = frame.shape[:2]
@ -114,21 +137,24 @@ while True:
print("Time spend")
print_timing("Starting up", 0)
print_timing("Importing face_recognition", 1)
print_timing("Opening the camera", 2)
print_timing("Opening the camera", 1)
print_timing("Importing face_recognition", 2)
print_timing("Searching for known face", 3)
print("\nResolution")
print(" Native: " + str(height) + "x" + str(width))
print(" Used: " + str(scale_height) + "x" + str(scale_width))
print("\nFrames searched: " + str(frames) + " (" + str(round(float(frames) / (timings[4] - timings[2]), 2)) + " fps)")
# Show the total number of frames and calculate the FPS by deviding it by the total scan time
print("\nFrames searched: " + str(frames) + " (" + str(round(float(frames) / (timings[4] - timings[3]), 2)) + " fps)")
print("Dark frames ignored: " + str(dark_tries))
print("Certainty of winning frame: " + str(round(match * 10, 3)))
exposures = ["long", "medium", "short"]
model_id = math.floor(float(match_index) / 3)
# Catch older 3-encoding models
if not match_index in models:
match_index = 0
print("Winning model: " + str(model_id) + " (\"" + models[model_id]["label"] + "\") using " + exposures[match_index % 3] + " exposure\n")
print("Winning model: " + str(match_index) + " (\"" + models[match_index]["label"] + "\")")
# End peacegully
stop(0)

6
config.ini
View file

@ -22,6 +22,12 @@ device_id = 1
# Speeds up face recognition but can make it less precise
max_height = 320
# Because of flashing IR emitters, some frames can be completely unlit
# Skip the frame if the lowest 1/8 of the histogram is above this percentage
# of the total
# The lower this setting is, the more dark frames are ignored
dark_threshold = 50
[debug]
# Show a short but detailed diagnostic report in console
end_report = false

2
installer.py
View file

@ -36,7 +36,7 @@ time.sleep(.5)
log("Installing required apt packages")
# Install packages though apt
handleStatus(subprocess.call(["apt", "install", "-y", "libpam-python", "fswebcam", "libopencv-dev", "python-opencv"]))
handleStatus(subprocess.call(["apt", "install", "-y", "git", "libpam-python", "fswebcam", "libopencv-dev", "python-opencv"]))
log("Starting camera check")