![]() ![]() > 268 return assert_less(zero, x, data=data, summarize=summarize) anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow_core/python/ops/check_ops.py in assert_positive(x, data, summarize, message, name)Ģ67 zero = ops.convert_to_tensor(0, dtype=x.dtype) anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow_core/python/ops/image_ops_impl.py in _CheckAtLeast3DImage(image, require_static) > 1339 assert_ops = _CheckAtLeast3DImage(image, require_static=False)ġ340 assert_ops = _assert(target_width > 0, ValueError, anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow_core/python/ops/image_ops_impl.py in _resize_image_with_pad_common(image, target_height, target_width, resize_fn)ġ337 raise ValueError('\'image\' must have either 3 or 4 dimensions.') anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow_core/python/ops/image_ops_impl.py in resize_image_with_pad_v2(image, target_height, target_width, method, antialias)ġ473 return _resize_image_with_pad_common(image, target_height, target_width, OperatorNotAllowedInGraphError Traceback (most recent call last)ĩ img_arr = tf.keras.Input(shape = (100,100,3))ġ0 tf.image.resize(img_arr, ) # works Tf.image.resize_with_pad(img_arr, 224, 224) # doesn't work Tf.image.resize_with_pad(img_arr, 224, 224) # works Img_path = 'D:/samples/your_bmp_image.With tensorflow 2.0, resize_with_pad does not seem to work when tf.keras.Input is given as an input, but resize works nicely. Processed_img_2 = tf.squeeze(processed_img,0) Processed_img = tf.image.crop_and_resize(img_4d,boxes= (A possible usecase would be sliding windows for example).īelow is a concrete implementation of the tf.image.crop_and_resize API. This option is useful if the zone you need to crop is not fully included in you original images. The pixels will take a RGB value of extrapolation_value%6 on each channel. Your original image is the little green dot in the upper left corner! The default value of the argument extrapolation_value is 0, so the values outside the frame of the original image are inferred as hence the black.īut if your usecase needs another value, you can provide it. With your example,, the coordinates you provide makes the red square. So, normalized coordinates outside are allowed. Normalized coordinates outside the range are allowed, in whichĬase we use extrapolation_value to extrapolate the input image values. plt.imshow( a.astype(np.uint8))Īs requested, I will dive a bit more into Cast your array to uint_8 to get an image that make sense. That's why you get those colored pixels (either solid red, solid green or solid blue, or a mixing of these). ![]() Out-of-range values will be clipped to these bounds.Īs you're using float outside the range, matplotlib is bounding your values to 1. Quoting the matplotlib documentation of plt.imshow (emphasis is mine):Īll values should be in the range for floats or įor integers. However, as that why matplotlib show you gibberish on your second attempt, it's just because you're using the wrong datatype. That's why you get a black box with your first set of coordinates (not normalized, and no extrapolation value provided), and not with your second set. The boxes argument needs normalized coordinates. Normalized coordinates outside the range are allowed, in which case we use extrapolation_value to We do allow y1 > y2, in which case the sampledĬrop is an up-down flipped version of the original image. Normalized image height is mapped to in image A normalized coordinate value of y is mapped to the imageĬoordinate at y * (image_height - 1), so as the interval of The i-th row of the tensor specifies the coordinates of a box in theīox_ind image and is specified in normalized coordinates [y1, x1, If I was wrong can anyone teach me how to use this function? thanks.Īctually, there's no problem with Tensorflow here.įrom the doc of tf.image.crop_and_resize (emphasis is mine) :īoxes: A Tensor of type float32. n(tf.global_variables_initializer())Īnd I handed in my origin img and result: a0, a1 #b = tf.image.crop_and_resize(img,],)Ĭ = tf.image.crop_and_resize(img_,boxes,box_ind,crop_size) Img_ = tf.Variable(img) # img shape is īoxes = tf.Variable(,]) I found tf.image.crop_and_resize can act as the ROI pooling layer.īut I try many times and cannot get the result that I expected.Or did the true result is exactly what I got? I'm working on the ROI pooling layer which work for fast-rcnn and I am used to use tensorflow.
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