Merge pull request #366 from andrewmv/darkness-error-reporting

Add darkness threshold feedback

src/cli/add.py

@@ -105,14 +105,24 @@ time.sleep(2)
 
 # Will contain found face encodings
 enc = []
-# Count the amount or read frames
+# Count the number of read frames
 frames = 0
+# Count the number of illuminated read frames
+valid_frames = 0
+# Count the number of illuminated frames that
+# were rejected for being too dark
+dark_tries = 0
+# Track the running darkness total
+dark_running_total = 0
+face_locations = None
+
 dark_threshold = config.getfloat("video", "dark_threshold")
 
 clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
 
 # Loop through frames till we hit a timeout
 while frames < 60:
+	frames += 1
 	# Grab a single frame of video
 	frame, gsframe = video_capture.read_frame()
 	gsframe = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
@@ -123,12 +133,23 @@ while frames < 60:
 	# All values combined for percentage calculation
 	hist_total = np.sum(hist)
 
-	# If the image is fully black or the frame exceeds threshold,
+	# Calculate frame darkness
+	darkness = (hist[0] / hist_total * 100)
+
+	# If the image is fully black due to a bad camera read,
 	# skip to the next frame
-	if hist_total == 0 or (hist[0] / hist_total * 100 > dark_threshold):
+	if (hist_total == 0) or (darkness == 100):
 		continue
 
-	frames += 1
+	# Include this frame in calculating our average session brightness
+	dark_running_total += darkness
+	valid_frames += 1
+
+	# If the image exceeds darkness threshold due to subject distance,
+	# skip to the next frame
+	if (darkness > dark_threshold):
+		dark_tries += 1
+		continue
 
 	# Get all faces from that frame as encodings
 	face_locations = face_detector(gsframe, 1)
@@ -137,12 +158,22 @@ while frames < 60:
 	if face_locations:
 		break
 
-# If more than 1 faces are detected we can't know wich one belongs to the user
+video_capture.release()
-if len(face_locations) > 1:
+
-	print("Multiple faces detected, aborting")
+# If we've found no faces, try to determine why
+if face_locations is None or not face_locations:
+	if valid_frames == 0:
+		print("Camera saw only black frames - is IR emitter working?")
+	elif valid_frames == dark_tries:
+		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))
+	else:
+		print("No face detected, aborting")
 	sys.exit(1)
-elif not face_locations:
+
-	print("No face detected, aborting")
+# If more than 1 faces are detected we can't know wich one belongs to the user
+elif len(face_locations) > 1:
+	print("Multiple faces detected, aborting")
 	sys.exit(1)
 
 face_location = face_locations[0]

src/compare.py

@@ -60,6 +60,8 @@ user = sys.argv[1]
 models = []
 # Encoded face models
 encodings = []
+# Amount of ignored 100% black frames
+black_tries = 0
 # Amount of ingnored dark frames
 dark_tries = 0
 # Total amount of frames captured
@@ -135,7 +137,9 @@ end_report = config.getboolean("debug", "end_report")
 
 # Start the read loop
 frames = 0
+valid_frames = 0
 timings["fr"] = time.time()
+dark_running_total = 0
 
 clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
 
@@ -145,7 +149,12 @@ while True:
 
 	# Stop if we've exceded the time limit
 	if time.time() - timings["fr"] > timeout:
-		sys.exit(11)
+		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)
+		else:
+      sys.exit(11)
 
 	# Grab a single frame of video
 	frame, gsframe = video_capture.read_frame()
@@ -157,9 +166,20 @@ while True:
 	# All values combined for percentage calculation
 	hist_total = np.sum(hist)
 
-	# If the image is fully black or the frame exceeds threshold,
+	# Calculate frame darkness
+	darkness = (hist[0] / hist_total * 100)
+
+	# If the image is fully black due to a bad camera read,
 	# skip to the next frame
-	if hist_total == 0 or (hist[0] / hist_total * 100 > dark_threshold):
+	if (hist_total == 0) or (darkness == 100):
+		black_tries += 1
+		continue
+
+	dark_running_total += darkness
+	valid_frames += 1
+	# If the image exceeds darkness threshold due to subject distance,
+	# skip to the next frame
+	if (darkness > dark_threshold):
 		dark_tries += 1
 		continue
 
@@ -218,6 +238,7 @@ while True:
 
 				# 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("Black frames ignored: %d " % (black_tries, ))
 				print("Dark frames ignored: %d " % (dark_tries, ))
 				print("Certainty of winning frame: %.3f" % (match * 10, ))
 

src/pam.py

@@ -50,6 +50,10 @@ def doAuth(pamh):
 	# Status 12 means we aborted
 	elif status == 12:
 		return pamh.PAM_AUTH_ERR
+	# Status 13 means the image was too dark
+	elif status == 13:
+		pamh.conversation(pamh.Message(pamh.PAM_ERROR_MSG, "Face detection image too dark"))
+		return pamh.PAM_AUTH_ERR
 	# Status 0 is a successful exit
 	elif status == 0:
 		# Show the success message if it isn't suppressed