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facedetection's Issues

Error

Constructing AdaBoost from existed model data
Construction finished

Traceback (most recent call last):
File "C:/Users/rpa-s/Desktop/FaceDetection-master/FaceDetection/EFace.py", line 27, in
rectangles = det.scanImgOverScale(imgSingleChannel)
File "C:\Users\rpa-s\Desktop\FaceDetection-master\FaceDetection\detector.py", line 114, in scanImgOverScale
rectangles += self.scanImgAtScale(model, image, scale)
File "C:\Users\rpa-s\Desktop\FaceDetection-master\FaceDetection\detector.py", line 54, in scanImgAtScale
subImages[idx] = Image(Mat = image[y:y+SEARCH_WIN_HEIGHT, x:x+SEARCH_WIN_WIDTH])
File "C:\Users\rpa-s\Desktop\FaceDetection-master\FaceDetection\image.py", line 33, in init
assert Mat != None
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

我更改了EFace中的TEST_IMG的路径到指定的文件上,运行之后出现了请问这个报错应该怎么处理...

MemoryError

If DEBUG_MODEL is True, the following code in cascade.py will crash:
self._mat = numpy.zeros((self.haar.featuresNum, tot_samples))

adaboost问题

你好!
请问一下,Adaboost.py文件,Adaboost类中,def train(self)方法中,有这样一句。

    for m in xrange(self.weakerLimit):
        self.N += 1

        if DEBUG_MODEL == True:
            weaker_start_time = time.time()

        self.G[m] = self.Weaker(self._mat, self._label, self.W)
        
        errorRate = self.G[m].train()

请问一下 self.G[m] = self.Weaker(self._mat, self._label, self.W),这样的话不是G[0],G[1],G[2],G[3]都是同一个弱分类器了吗?因为后面的参数都是一样的。谢谢!!!

I have a problem to ask author

作者
不好意思 現在我運行 trainingAdaboost.py 的時候已經可以訓練也保存完成了
但是在執行EFace.py的時候會產生 ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() 這樣的error
請問作者也有發生過同樣的問題?

关于弱分类器的阈值选择问题

你好,在实现弱分类器的时候,你用(miuPos + miuNeg)/2作为弱分类器的阈值,这个阈值是一个合理的选择吗?我修改了你的这部分代码,在特征里找了一些离散值来寻找最优的阈值,但是训练的时候选择到的特征和论文描述不符合

haarFeature.py相关问题咨询

请问vecRectSum中idxVector具体含义是什么?idx1,idx2,idx3,idx4代表的是什么呢?idx1,idx2,idx3,idx4计算式是何含义?
希望大神能抽空解除下我的疑惑,万分焦急!万分感谢!

More Powerful Machine and More training set resources

With the limitation of my own PC, I have used out of all training set which I have to build this face detection system -- EFace. If you have more powerful machine and be interesting in how to build a face detection system, just email me : )

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