Added the option to save snapshots

.gitignore

@@ -103,6 +103,9 @@ ENV/
 # generated models
 /src/models
 
+# snapshots
+/src/snapshots
+
 # build files
 debian/howdy.substvars
 debian/files

src/compare.py

@@ -16,6 +16,8 @@ import json
 import configparser
 import cv2
 import dlib
+import datetime
+import snapshot
 import numpy as np
 import _thread as thread
 from recorders.video_capture import VideoCapture
@@ -48,6 +50,18 @@ def init_detector(lock):
 	lock.release()
 
 
+def make_snapshot(type):
+	"""Generate snapshot after detection"""
+	snapshot.generate(snapframes, [
+		type + " LOGIN",
+		"Date: " + datetime.datetime.utcnow().strftime("%Y/%m/%d %H:%M:%S UTC"),
+		"Scan time: " + str(round(time.time() - timings["fr"], 2)) + "s",
+		"Frames: " + str(frames) + " (" + str(round(frames / (time.time() - timings["fr"]), 2)) + "FPS)",
+		"Hostname: " + os.uname().nodename,
+		"Best certainty value: " + str(round(lowest_certainty * 10, 1))
+	])
+
+
 # Make sure we were given an username to tast against
 if len(sys.argv) < 2:
 	sys.exit(12)
@@ -67,7 +81,11 @@ black_tries = 0
 dark_tries = 0
 # Total amount of frames captured
 frames = 0
-# face recognition/detection instances
+# Captured frames for snapshot capture
+snapframes = []
+# Tracks the lowest certainty value in the loop
+lowest_certainty = 10
+# Face recognition/detection instances
 face_detector = None
 pose_predictor = None
 face_encoder = None
@@ -95,6 +113,8 @@ timeout = config.getint("video", "timeout", fallback=5)
 dark_threshold = config.getfloat("video", "dark_threshold", fallback=50.0)
 video_certainty = config.getfloat("video", "certainty", fallback=3.5) / 10
 end_report = config.getboolean("debug", "end_report", fallback=False)
+capture_failed = config.getboolean("snapshots", "capture_failed", fallback=False)
+capture_successful = config.getboolean("snapshots", "capture_successful", fallback=False)
 
 # Save the time needed to start the script
 timings["in"] = time.time() - timings["st"]
@@ -150,7 +170,11 @@ while True:
 
 	# Stop if we've exceded the time limit
 	if time.time() - timings["fr"] > timeout:
-		if (dark_tries == valid_frames):
+		# Create a timeout snapshot if enabled
+		if capture_failed:
+			make_snapshot("FAILED")
+
+		if dark_tries == valid_frames:
 			print("All frames were too dark, please check dark_threshold in config")
 			print("Average darkness: " + str(dark_running_total / valid_frames) + ", Threshold: " + str(dark_threshold))
 			sys.exit(13)
@@ -159,9 +183,14 @@ while True:
 
 	# Grab a single frame of video
 	frame, gsframe = video_capture.read_frame()
-
 	gsframe = clahe.apply(gsframe)
 
+	# If snapshots have been turned on
+	if capture_failed or capture_successful:
+		# Start capturing frames for the snapshot
+		if len(snapframes) < 3:
+			snapframes.append(frame)
+
 	# Create a histogram of the image with 8 values
 	hist = cv2.calcHist([gsframe], [0], None, [8], [0, 256])
 	# All values combined for percentage calculation
@@ -210,10 +239,14 @@ while True:
 		match_index = np.argmin(matches)
 		match = matches[match_index]
 
+		# Update certainty if we have a new low
+		if lowest_certainty > match:
+			lowest_certainty = match
+
 		# Check if a match that's confident enough
 		if 0 < match < video_certainty:
 			timings["tt"] = time.time() - timings["st"]
-			timings["fr"] = time.time() - timings["fr"]
+			timings["fl"] = time.time() - timings["fr"]
 
 			# If set to true in the config, print debug text
 			if end_report:
@@ -227,7 +260,7 @@ while True:
 				print("  Open cam + load libs: %dms" % (round(max(timings["ll"], timings["ic"]) * 1000, )))
 				print_timing("  Opening the camera", "ic")
 				print_timing("  Importing recognition libs", "ll")
-				print_timing("Searching for known face", "fr")
+				print_timing("Searching for known face", "fl")
 				print_timing("Total time", "tt")
 
 				print("\nResolution")
@@ -238,13 +271,17 @@ while True:
 				print("  Used: %dx%d" % (scale_height, scale_width))
 
 				# Show the total number of frames and calculate the FPS by deviding it by the total scan time
-				print("\nFrames searched: %d (%.2f fps)" % (frames, frames / timings["fr"]))
+				print("\nFrames searched: %d (%.2f fps)" % (frames, frames / timings["fl"]))
 				print("Black frames ignored: %d " % (black_tries, ))
 				print("Dark frames ignored: %d " % (dark_tries, ))
 				print("Certainty of winning frame: %.3f" % (match * 10, ))
 
 				print("Winning model: %d (\"%s\")" % (match_index, models[match_index]["label"]))
 
+			# Make snapshot if enabled
+			if capture_successful:
+				make_snapshot("SUCCESSFUL")
+
 			# End peacefully
 			sys.exit(0)
 

src/config.ini

@@ -30,6 +30,7 @@ use_cnn = false
 [video]
 # The certainty of the detected face belonging to the user of the account
 # On a scale from 1 to 10, values above 5 are not recommended
+# Lower is better
 certainty = 3.5
 
 # The number of seconds to search before timing out
@@ -37,7 +38,7 @@ timeout = 4
 
 # The path of the device to capture frames from
 # Should be set automatically by an installer if your distro has one
-device_path = "/dev/video1"
+device_path = none
 
 # Scale down the video feed to this maximum height
 # Speeds up face recognition but can make it less precise
@@ -73,6 +74,14 @@ force_mjpeg = false
 # OPENCV only.
 exposure = -1
 
+[snapshots]
+# Capture snapshots of failed login attempts and save them to disk with metadata
+# Snapshots are saved to the "snapshots" folder
+capture_failed = true
+
+# Do the same as the option above but for successful attemtps
+capture_successful = true
+
 [debug]
 # Show a short but detailed diagnostic report in console
 # Enabling this can cause some UI apps to fail, only enable it to debug

src/snapshot.py

@@ -0,0 +1,59 @@
+# Create and save snapshots of auth attemtps
+
+# Import modules
+import cv2
+import os
+import datetime
+import numpy as np
+
+
+def generate(frames, text_lines):
+	"""Generate a shapshot from given frames"""
+
+	# Don't execute if no frames were given
+	if len(frames) == 0:
+		return
+
+	# Get the path to the containing folder
+	abpath = os.path.dirname(os.path.abspath(__file__))
+	# Get frame dimentions
+	frame_height, frame_width, cc = frames[0].shape
+	# Spread the given frames out horizontally
+	snap = np.concatenate(frames, axis=1)
+
+	# Create colors
+	pad_color = [44, 44, 44]
+	text_color = [255, 255, 255]
+
+	# Add a gray square at the bottom of the image
+	snap = cv2.copyMakeBorder(snap, 0, len(text_lines) * 20 + 40, 0, 0, cv2.BORDER_CONSTANT, value=pad_color)
+
+	# Add the Howdy logo if there's space to do so
+	if len(frames) > 1:
+		# Load the logo from file
+		logo = cv2.imread(abpath + "/logo.png")
+		# Calculate the position of the logo
+		logo_y = frame_height + 20
+		logo_x = frame_width * len(frames) - 210
+
+		# Overlay the logo on top of the image
+		snap[logo_y:logo_y+57, logo_x:logo_x+180] = logo
+
+	# Go through each line
+	line_number = 0
+	for line in text_lines:
+		# Calculate how far the line should be from the top
+		padding_top = frame_height + 30 + (line_number * 20)
+		# Print the line onto the image
+		cv2.putText(snap, line, (30, padding_top), cv2.FONT_HERSHEY_SIMPLEX, .4, text_color, 0, cv2.LINE_AA)
+
+		line_number += 1
+
+	# Made sure a snapshot folder exist
+	if not os.path.exists(abpath + "/snapshots"):
+		os.makedirs(abpath + "/snapshots")
+
+	# Generate a filename based on the current time
+	filename = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%S.jpg")
+	# Write the image to that file
+	cv2.imwrite(abpath + "/snapshots/" + filename, snap)