109 lines
4.4 KiB
Python
109 lines
4.4 KiB
Python
import os
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os.environ["OMP_NUM_THREADS"] = str(1)
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import dshowcapture
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import cv2
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import multiprocessing
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import time
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import numpy as np
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import math
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#this is just here for conveience
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def startProcess(frameQueue, faceQueue, fps, targetBrightness, width, height, mirrorInput):
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webcam = Webcam(frameQueue, faceQueue, fps, targetBrightness, width, height, mirrorInput)
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webcam.start()
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class Webcam():
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def __init__(self, frameQueue, faceQueue, fps, targetBrightness, width, height, mirrorInput):
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if os.name == 'nt':
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self.cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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else:
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self.cap = cv2.VideoCapture(0)
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if height > 480:
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self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
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self.webcamBuffer = 2
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self.cap.set(38, self.webcamBuffer)
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self.cap.set(3, width)
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self.cap.set(4, height)
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self.cap.set(cv2.CAP_PROP_FPS, fps)
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self.targetFrameTime = 1. / fps
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self.gamma = 0.7
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self.mirror = mirrorInput
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self.frameQueue = frameQueue #outgoing frames from the webcam
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self.faceQueue = faceQueue #incoming face data for gamma calculation
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self.cameraLatency = 0
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self.brightnessFrame = None #a copy of the brightness channel for gamma calculations
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self.targetBrightness = targetBrightness #the target average brightness of the face, used in gamma calculations
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self.width = width #unused, but it seemed useful to have around
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self.height = height #unused, but it seemed useful to have around
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self.ret = 0
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self.frame = None
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def start(self):
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while self.cap.isOpened():
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frameStart= time.perf_counter()
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totalFrameLatency = time.perf_counter()
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self.getFrame()
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self.cameraLatency = time.perf_counter() - frameStart
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if self.ret:
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self.applyGamma()
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if self.mirror:
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self.frame = cv2.flip(self.frame, 1)
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self.frameQueue.put([self.frame, self.cameraLatency, totalFrameLatency])
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if self.faceQueue.qsize() > 0:
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self.updateGamma()
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sleepTime = self.targetFrameTime - (time.perf_counter() - frameStart)
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sleepTime = max(sleepTime, 0)
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time.sleep(sleepTime)
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def getFrame(self):
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#My webcam's gain tends to be noisy
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#and my gamma correction seems less noisy
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#so I tell the camera not to apply gain
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self.cap.set(cv2.CAP_PROP_GAIN, 0)
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#keeping track of how long it takes to get frames from the webcam
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#because that one line is the cause of 90% of late frames
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cameraStart = time.perf_counter()
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self.ret, self.frame = self.cap.read()
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self.cameraLatency = time.perf_counter() - cameraStart
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return
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#Applies a gamma curve to the frame
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#Uses a lookup table because that's way faster than actually calculating every pixel
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#Immensely improves tracking in low light situations
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def applyGamma(self):
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img_yuv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2YUV)
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#saving a copy of the brightness channel so I can use it to adjust the gamma based on where the face is in that frame
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self.brightnessFrame = img_yuv[:,:,0].copy()
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#building a lookup table on the fly
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lookupTable = np.array(range(256))
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loopupTable = lookupTable/255
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lookupTable = np.power(loopupTable, self.gamma)*255
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img_yuv[:,:,0] = lookupTable[img_yuv[:,:,0]]
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#I convert the image to RBG here because it was getting repeatedly converted in the face tracking
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self.frame = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2RGB)
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#calculate the ideal gamma based on the brightness of the user's face
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#kind of winging it with the math, but it's a lot better than calculating based on the whole frame
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def updateGamma(self):
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face_center, face_radius = self.faceQueue.get()
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y1 = int(face_center[1] - face_radius[1])
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y2 = int(face_center[1] + face_radius[1])
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x1 = int(face_center[0] - face_radius[0])
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x2 = int(face_center[0] + face_radius[0])
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#I was doing random sampling of the image brightness
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#and that's probably brighter for the whole frame
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#but for just the face this is pretty fast
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averageBrightness = np.mean(self.brightnessFrame[y1:y2,x1:x2])/256
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self.gamma = math.log(self.targetBrightness, averageBrightness)
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return
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