tools_matrix.py 文件源码

def detect_face_12net(cls_prob,roi,out_side,scale,width,height,threshold):
    in_side = 2*out_side+11
    stride = 0
    if out_side != 1:
        stride = float(in_side-12)/(out_side-1)
    (x,y) = np.where(cls_prob>=threshold)
    boundingbox = np.array([x,y]).T
    bb1 = np.fix((stride * (boundingbox) + 0 ) * scale)
    bb2 = np.fix((stride * (boundingbox) + 11) * scale)
    boundingbox = np.concatenate((bb1,bb2),axis = 1)
    dx1 = roi[0][x,y]
    dx2 = roi[1][x,y]
    dx3 = roi[2][x,y]
    dx4 = roi[3][x,y]
    score = np.array([cls_prob[x,y]]).T
    offset = np.array([dx1,dx2,dx3,dx4]).T
    boundingbox = boundingbox + offset*12.0*scale
    rectangles = np.concatenate((boundingbox,score),axis=1)
    rectangles = rect2square(rectangles)
    pick = []
    for i in range(len(rectangles)):
    x1 = int(max(0     ,rectangles[i][0]))
    y1 = int(max(0     ,rectangles[i][1]))
    x2 = int(min(width ,rectangles[i][2]))
    y2 = int(min(height,rectangles[i][3]))
    sc = rectangles[i][4]
    if x2>x1 and y2>y1:
        pick.append([x1,y1,x2,y2,sc])
    return NMS(pick,0.5,'iou')