Merge pull request #8 from Boltgolt/dev

A wide range of fixes and improvements

README.md

@@ -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.
 

cli.py

@@ -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
+# Check if if a command has been given and print help otherwise
-if (len(sys.argv) < 3):
+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
+# Call the right files for commands that don't need root
-if cmd in ["list", "add", "remove", "clear"] and os.getenv("SUDO_USER") is None:
+if cmd == "help":
-	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":
 	print("Howdy IR face recognition")
 	import cli.help
-elif cmd == "add":
+elif cmd == "test":
-	import cli.add
+	import cli.test
-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
+	# Check if the minimum of 3 arugemnts has been met and print help otherwise
-	if sys.argv[1] in ["list", "add", "remove", "clear", "help"]:
+	if (len(sys.argv) < 3):
-		print("Usage: howdy <user> <command>")
+		print("Howdy IR face recognition help")
-	else:
-		print('Unknown command "' + cmd + '"')
 		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

cli/__init__.py

@@ -0,0 +1 @@
+# Marks this folder as importable

cli/add.py

@@ -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
+# Will contain found face encodings
-for delay in [30, 6, 0]:
+enc = []
-	time.sleep(.3)
+# Count the amount or read frames
-	captureFrame(delay)
+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
+# Give let the user know how it went
-os.remove(tmp_file)
+print("Scan complete")
-
+print("\nAdded a new model to " + user)
-print("Done.")

cli/help.py

@@ -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\

cli/test.py

@@ -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()

compare.py

@@ -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
+# Add the time needed to start the script
-timings.append(time.time())
-import face_recognition
 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", 1)
-					print_timing("Opening the camera", 2)
+					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"]
+					# Catch older 3-encoding models
-					model_id = math.floor(float(match_index) / 3)
+					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)

config.ini

@@ -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

installer.py

@@ -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")