OpenSeeFace/facetracker.py

#import copy
import os
#limits the threads used by numpy to 1
#setting this and the landmarks model to use 1 means the cpu usage is pretty low

os.environ["OMP_NUM_THREADS"] = str(1)
import argparse
import traceback
import threading
import queue
import time
from tracker import Tracker
import multiprocessing
import webcam
import vts
import preview
import cv2

#overall, my changes seem to have memory usage up from about 140mb to 180mb
#but cpu usage seems to be down
#and it's more resilent to low performance scenarios

parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-i", "--ip", help="Set IP address for sending tracking data", default="127.0.0.1")
parser.add_argument("-p", "--port", type=int, help="Set port for sending tracking data", default=11573)
parser.add_argument("-W", "--width", type=int, help="Set raw RGB width", default=640)
parser.add_argument("-H", "--height", type=int, help="Set raw RGB height", default=360)
parser.add_argument("-F", "--fps", type=int, help="Set camera frames per second", default=24)
parser.add_argument("-c", "--capture", help="Set camera ID (0, 1...) or video file", default=0)
parser.add_argument("-M", "--mirror-input", action="store_true", help="Process a mirror image of the input video")
parser.add_argument("-t", "--threshold", type=float, help="Set minimum confidence threshold for face tracking", default=0.6)
parser.add_argument("-d", "--detection-threshold", type=float, help="Set minimum confidence threshold for face detection", default=0.6)
parser.add_argument("-s", "--silent", type=int, help="Set this to 1 to prevent text output on the console", default=0)
parser.add_argument("--model", type=int, help="This can be used to select the tracking model. Higher numbers are models with better tracking quality, but slower speed, except for model 4, which is wink optimized.", default=3, choices=[0, 1, 2, 3, 4])
parser.add_argument("--preview", type=int, help="Preview the frames sent to the tracker", default=0)
parser.add_argument("--feature-type", type=int, help="Sets which version of feature extraction is used. 0 is my new version that works well for me and allows for some customization, 1 is EmilianaVT's version aka, normal OpenSeeFace operation", default=0, choices=[0, 1])
parser.add_argument("--numpy-threads", type=int, help="Numer of threads Numpy can use, doesn't seem to effect much", default=1)
parser.add_argument("--landmark-detection-threads", type=int, help="Numer of threads used for landmark detection. Default is 1 (~15ms per frame on my computer), 2 gets slightly faster frames (~10ms on my computer), more than 2 doesn't seem to help much", default=1)
parser.add_argument("-v", "--visualize", type=int, help="Set this to 1 to visualize the tracking", default=0)


args = parser.parse_args()

os.environ["OMP_NUM_THREADS"] = str(args.landmark_detection_threads)

def messaging(messageQueue):
    while True:
        message = messageQueue.get()
        print(message)


def visualize(frame, face, previewFrameQueue, face_center, face_radius):

    y1 = int(face_center[1] - face_radius[1])
    y2 = int(face_center[1] + face_radius[1])
    x1 = int(face_center[0] - face_radius[0])
    x2 = int(face_center[0] + face_radius[0])
    bbox = (y1, x1, y2 - y1, x2 - x1)
    frame = cv2.putText(frame, str(face.id), (int(bbox[1]), int(bbox[0])), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255,0,255))
    frame = cv2.putText(frame, f"{face.conf:.4f}", (int(bbox[1] + 18), int(bbox[0] - 6)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,0,255))
    for pt_num, (x,y,c) in enumerate(face.lms):
        x = int(x + 0.5)
        y = int(y + 0.5)
        frame = cv2.putText(frame, str(pt_num), (int(y), int(x)), cv2.FONT_HERSHEY_SIMPLEX, 0.25, (255,255,0))
        color = (0, 255, 0)
        if pt_num >= 66:
            color = (255, 255, 0)
        if not (x < 0 or y < 0 or x >= height or y >= width):
            cv2.circle(frame, (y, x), 1, color, -1)
    previewFrameQueue.put(frame)


#processing args
#For Vtube Studio: ip 127.0.0.1 port 11573
target_ip = args.ip
target_port = args.port
fps = args.fps
height = args.height
width = args.width
previewFlag = (args.preview == 1)
mirror_input = args.mirror_input
featureType = args.feature_type
visualizeFlag = (args.visualize == 1)

#I make a minor tweak to the framerate to help the camera out
#it's an inpercetible amount of time per frame
#but my hope is it keeps things from riding up against what the webcam can put out
target_duration = 1. / (fps-0.01)
targetBrightness = 0.55
silent = (args.silent == 1)


#increating the webcam queue to compensate for longer frame times at different settings
frameQueueSize = 1
if height > 480:
    frameQueueSize = 3
    #My testing has shown that it can take upwards of 80ms when capturing images above 480p
    #each frame is 33ms, so I'd need 3 frames, or 99ms to cover that without skipping a frame
if fps > 30:
    frameQueueSize = frameQueueSize * 2
    #I'm just going to assume the webcam does not work well above 30fps

#creating queues
frameQueue = multiprocessing.Queue(maxsize=frameQueueSize)
faceQueue = multiprocessing.Queue(maxsize=1)
messageQueue = queue.Queue()
packetQueue = queue.Queue()

#creating threads
#probably doesn't need it's own thread, but it can't hurt
messageThread = threading.Thread(target = messaging, args = [ messageQueue],)
messageThread.daemon = True
messageThread.start()

#I want to decouple the requests so they don't block anything else if they're slow
VTS = vts.VTS(target_ip, target_port, silent, height, width, packetQueue, messageQueue)
packetSenderThread = threading.Thread(target = VTS.packetSender, args = [],)
packetSenderThread.daemon = True
packetSenderThread.start()

#creating processes
#not a very active process, it's blocked by the main thread 90% of the time
#but putting it in it's own process means there's a little bit less occupying the same core as the main thread
#which helps when the system is under load
webcamProcess = multiprocessing.Process(target=webcam.startProcess,args = (frameQueue, faceQueue, fps, targetBrightness, width, height,  mirror_input,messageQueue, ))
webcamProcess.daemon = True
webcamProcess.start()

#This is it's own process because it's super weird performance wise
if previewFlag or visualizeFlag:
    previewFrameQueue = multiprocessing.Queue(maxsize=1)
    previewProcess = multiprocessing.Process(target=preview.startProcess, args = (previewFrameQueue, target_duration,))
    previewProcess.daemon = True
    previewProcess.start()

frame_count = 0
peak_frame_time=0.0
total_active_time = 0.0
total_run_time = 0.0
frame_start = 0.0
peak_time_between = 0.0
peak_camera_latency = 0.0
total_camera_latency = 0.0
failures = 0

tracker = Tracker(width, height, messageQueue, featureType,  threshold=args.threshold, silent=silent, model_type=args.model, detection_threshold=args.detection_threshold)

#don't start until the webcam is ready
while frameQueue.qsize() < 1:
    time.sleep(0.33)
try:
    while True:
        #clearing these so there's no stale data to confuse the checks
        packet = None
        face = None
        frame_start = time.perf_counter()
        frame_count += 1
        #if the camera process doesn't have a frame for us we just skip everything
        #in my testing the camera was consistently waiting on the main process
        #so if it's *that far behind* it probably needs a frame or two to catch up
        if frameQueue.qsize() > 0:
            frame, camera_latency = frameQueue.get()

            #If I don't wait a few frames to start tracking I get wild peak frame times, like 500ms
            if frame_count > 5:
                peak_camera_latency = max(camera_latency, peak_camera_latency)
                total_camera_latency+= camera_latency

            if previewFlag:
                #there's no reason to ever wait on this process or give it much of a queue
                if previewFrameQueue.qsize() < 1:
                    previewFrameQueue.put(frame)

            try:
                face, face_center, face_radius = tracker.predict(frame)
                failures = 0
            except Exception as e:
                if e.__class__ == KeyboardInterrupt:
                    if not silent:
                        messageQueue.put("Quitting")
                    break
                traceback.print_exc()
                failures += 1
                if failures > 30:
                    break

            if face is not None:
                packet = VTS.preparePacket(face)
                #this can maybe cause weirdness in the gamma calculations
                #but I don't want to let that build a queue
                if faceQueue.qsize() < 1:
                    faceQueue.put([face_center, face_radius])
                if visualizeFlag:
                    visualize(frame, face, previewFrameQueue, face_center, face_radius)
        else:
            messageQueue.put("Camera behind, skipping frame")

        duration = time.perf_counter() - frame_start

        total_active_time += duration
        peak_frame_time = max(peak_frame_time, duration)
        if duration < target_duration:
            time.sleep(target_duration - duration)
        else:
            #all caps because if this is consistently happening it's an issue
            messageQueue.put("CANNOT MAINTAIN FRAMERATE!")

        peak_time_between = max(peak_time_between, time.perf_counter() -frame_start)
        total_run_time += time.perf_counter() -frame_start

        #If we don't have something to send to Vtube Studio we don't
        if(packet is not None):
            packetQueue.put(packet)
        else:
            messageQueue.put("No data sent to VTS")

except KeyboardInterrupt:
    if not silent:
        print("Quitting")

#printing statistics on close
#it makes identifying problems easier
#480p generally results in camera latencies between 0.5ms and 2.0ms
print(f"Peak camera latency: {(peak_camera_latency*1000):.3f}ms")
print(f"Average camera latency: {(total_camera_latency*1000/(frame_count-5)):.3f}ms")
#The longest time a full cycle of the main loop takes, including sleep
#indicates issues when higher than average time between frames by a significant amount
print(f"Peak time between frames: {(peak_time_between*1000):.3f}ms")
#at 30fps this should be a hair above 33.333ms
print(f"Average time between frames: {(total_run_time*1000/(frame_count)):.3f}ms")
#These measure how long the main loop takes *not including sleep*
#tends to be slightly longer than the average time given by the tracking status by 1ms to 2ms
#because it includes detection frames and all the other minor operations that need to happen every frame
print(f"Peak frame time: {(peak_frame_time*1000):.3f}ms")
print(f"Average frame time: { ((total_active_time)*1000/(frame_count)):.3f}ms")
print(f"Run time (seconds): {total_run_time:.2f} s\nFrames: {frame_count}")

webcamProcess.terminate()
if previewFlag:
    previewProcess.terminate()
os._exit(0)