示例#1
0
def resize(src):
    height, width, _ = src.shape
    dst = src
    if height != width:
        if a.pad:
            size = max(height, width)
            # pad to correct ratio
            oh = (size - height) // 2
            ow = (size - width) // 2
            dst = im.pad(image=dst,
                         offset_height=oh,
                         offset_width=ow,
                         target_height=size,
                         target_width=size)
        else:
            # crop to correct ratio
            size = min(height, width)
            oh = (height - size) // 2
            ow = (width - size) // 2
            dst = im.crop(image=dst,
                          offset_height=oh,
                          offset_width=ow,
                          target_height=size,
                          target_width=size)

    assert (dst.shape[0] == dst.shape[1])

    size, _, _ = dst.shape
    if size > a.size:
        dst = im.downscale(images=dst, size=[a.size, a.size])
    elif size < a.size:
        dst = im.upscale(images=dst, size=[a.size, a.size])
    return dst
def resize(src):
    height, width, _ = src.shape
    dst = src
    if height != width:
        if a.pad:
            size = max(height, width)
            # pad to correct ratio
            oh = (size - height) // 2
            ow = (size - width) // 2
            dst = im.pad(image=dst, offset_height=oh, offset_width=ow, target_height=size, target_width=size)
        else:
            # crop to correct ratio
            size = min(height, width)
            oh = (height - size) // 2
            ow = (width - size) // 2
            dst = im.crop(image=dst, offset_height=oh, offset_width=ow, target_height=size, target_width=size)

    assert(dst.shape[0] == dst.shape[1])

    size, _, _ = dst.shape
    if size > a.size:
        dst = im.downscale(images=dst, size=[a.size, a.size])
    elif size < a.size:
        dst = im.upscale(images=dst, size=[a.size, a.size])
    return dst
def resize(src, tfUpscaleInterpolationType):
    height, width, _ = src.shape
    dst = src
    if a.preserve_aspect_ratio:
        if height != width:
            if a.pad:
                size = max(height, width)
                # pad to correct ratio
                oh = (size - height) // 2
                ow = (size - width) // 2
                dst = im.pad(image=dst,
                             offset_height=oh,
                             offset_width=ow,
                             target_height=size,
                             target_width=size)
            else:
                # crop to correct ratio
                size = min(height, width)
                oh = (height - size) // 2
                ow = (width - size) // 2
                dst = im.crop(image=dst,
                              offset_height=oh,
                              offset_width=ow,
                              target_height=size,
                              target_width=size)

                assert (dst.shape[0] == dst.shape[1])

                size, _, _ = dst.shape
                if size > a.fm_input_size:
                    dst = im.downscale(images=dst,
                                       size=[a.fm_input_size, a.fm_input_size])
                elif size < a.fm_input_size:
                    if tfUpscaleInterpolationType == tf.image.ResizeMethod.BICUBIC:
                        dst = im.upscale(
                            images=dst,
                            size=[a.fm_input_size, a.fm_input_size])
                    elif tfUpscaleInterpolationType == tf.image.ResizeMethod.NEAREST_NEIGHBOR:
                        dst = im.upscaleWithNearestNeighborInterpolation(
                            images=dst,
                            size=[a.fm_input_size, a.fm_input_size])
    else:
        if tfUpscaleInterpolationType == tf.image.ResizeMethod.BICUBIC:
            dst = im.upscale(images=dst,
                             size=[a.fm_input_size, a.fm_input_size])
        elif tfUpscaleInterpolationType == tf.image.ResizeMethod.NEAREST_NEIGHBOR:
            dst = im.upscaleWithNearestNeighborInterpolation(
                images=dst, size=[a.fm_input_size, a.fm_input_size])

    return dst
示例#4
0
def resize(src):
    height,width,_ =src.shape
    dst = src
    if height != width:
        if a.pad: #如果需要padding,以较长边padding
            size = max(height,width)
            oh = (size - height) // 2
            ow = (size - width) // 2
            dst = im.pad(image = dst,offset_height = oh, offset_width = ow,target_height = size,target_width = size)
         else: #否则,以较短边修剪image
             size = min(height,width)
             oh = (height - size) //2
             ow = (width - size) //2
             dst = im.crop(image=dst,offset_height=oh,offset_width=ow,target_height=size,target_width=size)
    assert(dst.shape[0] == dst.shape[1])

    size,_,_ = dst.shape
    if size > a.size:#如果修剪后的image尺寸与a.size不符合,则进行相应的修改
        dst = im.downscale(images=dst,size=[a.size,a.size])
    elif size < a.size:
        dst = im.upscale(images=dst,size=[a.size,a.size])
    return dst