camera.capture(stream, "rgb")
img=stream.array
#Convert to intensity
grey = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#Remove faulty pixels
start = time.time()
#Load images cached
gallery.append(grey)
if (len(gallery) > 4):
gallery.pop(0)
faulty = pixcheck.pixcheck(list(gallery))
subtracted = numpy.count_nonzero(faulty)
print("Subtracted pixels: "+str(subtracted))
#save this
cv2.imwrite(name+"/faulty.bmp",faulty)
end= time.time()
print("Faulty/noisy pixel check time: "+str(end-start))
#find objects
high_threshold = 100
low_threshold = 5
counts = counter.giveCount(img,low_threshold,high_threshold)
rate = counts*framerate
print("Rate: "+str(rate))
show.draw(rate,subtracted)
print ("Img "+str(i))
i+=1
stream.seek(0)
stream.truncate()
camera.capture(stream, "rgb")
img = stream.array
#Convert to intensity
grey = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#Remove faulty pixels
start = time.time()
#Load images cached
gallery.append(grey)
if (len(gallery) > 4):
gallery.pop(0)
faulty = pixcheck.pixcheck(list(gallery))
subtracted = numpy.count_nonzero(faulty)
print("Subtracted pixels: " + str(subtracted))
#save this
cv2.imwrite(name + "/faulty.bmp", faulty)
end = time.time()
print("Faulty/noisy pixel check time: " + str(end - start))
#find objects
high_threshold = 100
low_threshold = 5
counts = counter.giveCount(img, low_threshold, high_threshold)
rate = counts * framerate
print("Rate: " + str(rate))
show.draw(rate, subtracted)
print("Img " + str(i))
i += 1
stream.seek(0)
stream.truncate()
W = T * Pp return W # 开始测试，输入测试样例和权值矩阵,返回输出的矩阵并且转换为数组 def start(pt,W): pt = np.matrix(pt) n = W * pt.T n = n.getA1() a = [] for i in n: a.append(hardlims(i)) return a if __name__ == '__main__': W = training() # print W test = [-1,1,1,1,-1, 1,-1,-1,-1,1, 1,-1,-1,-1,1, -1,-1,-1,-1,-1, -1,-1,-1,-1,-1, -1,-1,-1,-1,-1] show.draw(test) pt = np.matrix(test) n = pt * W n = n.getA1() a = [] for i in n: a.append(hardlims(i)) # a = np.matrix(a) show.draw(a)
