car_recognizer.py 文件源码

def thresholdImage(gray,thr_type,thr,block_size=None,img=None):

    """ Where thr_type in {1,2,3,4}
        1: Normal threshold
        2: Otsu
        3: Adaptive (mean)
        4: Adaptive (Gaussian)
        More thresholds: Using two thresholds taking into account that most pixels are from the floor 
            (Trying to don't erase black cars)
        5: Double threshold (using percentiles) 
        6: Double threshold (using manually set values)
    """
    if thr_type == 1: 
        ret,thr = cv2.threshold(gray,thr,255,cv2.THRESH_BINARY)
        return thr
    elif thr_type == 2:
        ret,thr = cv2.threshold(gray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # Black/red cars desapeared. Good for Segmentation of background
        return thr
    elif thr_type == 3:
        return cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,block_size,2) # Less noise, but can't recognize all cars
    elif thr_type == 4:
        return cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,block_size,2) # More noise, more cars
    elif thr_type == 5:
        firstQ = np.percentile(gray2,65) # Return de value of the pixel that corresponds to the 65 percent of all sorted pixels in grayscale
        secondQ = np.percentile(gray2,50)
        thirdQ = np.percentile(gray2,35)
        return applyThreshold(gray,firstQ,thirdQ)
    elif thr_type == 6:
        return applyThreshold(gray,40,113)
    elif thr_type == 7:
        rows,col = img[:,:,0].shape
        r1,g1,b1 = getChannels(gray) # Actually is not grayscale but a BGR image (just a name)
        r2,g2,b2 = getChannels(img)
        res = np.zeros((rows,col))
        for i in range(0,rows):
            for j in range(0,col):
                rDif = abs(int(r1[i,j]) - int(r2[i,j]))
                gDif = abs(int(g1[i,j]) - int(g2[i,j]))
                bDif = abs(int(b1[i,j]) - int(b2[i,j]))
                if rDif >= thr or gDif >= thr or bDif >= thr:
                    res[i,j] = 0
                else:
                    res[i,j] = 255
        return res

    else:
        return None