The ESPCNCallback object will compute and display merge ( 'YCbCr', ( out_img_y, out_img_cb, out_img_cr )). uint8 ( out_img_y ), mode = 'L' ) out_img_cb = cb. predict ( input ) out_img_y = out out_img_y *= 255.0 # Restore the image in RGB color space. expand_dims ( y, axis = 0 ) out = model. BICUBIC, ) def upscale_image ( model, img ): '''Predict the result based on input image and restore the image as RGB.''' ycbcr = img. show () def get_lowres_image ( img, upscale_factor ): '''Return low-resolution image to use as model input.''' return img. savefig ( str ( prefix ) + '-' + title + '.png' ) plt. mark_inset ( ax, axins, loc1 = 1, loc2 = 3, fc = 'none', ec = 'blue' ) plt. xticks ( visible = False ) # Make the line. set_xlim ( x1, x2 ) # Apply the y-limits. imshow ( img_array, origin = 'lower' ) # Specify the limits. title ( title ) # zoom-factor: 2.0, location: upper-left axins = zoomed_inset_axes ( ax, 2, loc = 2 ) axins. imshow ( img_array, origin = 'lower' ) plt. astype ( 'float32' ) / 255.0 # Create a new figure with a default 111 subplot. Import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes from mpl_toolkits.axes_grid1.inset_locator import mark_inset import PIL def plot_results ( img, prefix, title ): '''Plot the result with zoom-in area.''' img_array = img_to_array ( img ) img_array = img_array. In this function, we use the y channel from the YUV color spaceĪs input to the model and then combine the output with the upscale_image to turn a low-resolution image toĪ high-resolution version reconstructed by the model.get_lowres_image to convert an image to its low-resolution version.We need to define several utility functions to monitor our results: Conv2D ( channels * ( upscale_factor ** 2 ), 3, ** conv_args )( x ) outputs = DepthToSpace ( upscale_factor )( x ) return keras. Conv2D ( 32, 3, ** conv_args )( x ) x = layers. Conv2D ( 64, 3, ** conv_args )( x ) x = layers. Conv2D ( 64, 5, ** conv_args )( inputs ) x = layers. Input ( shape = ( None, None, channels )) x = layers. reshape ( x, ) return x def get_model ( upscale_factor = 3, channels = 1 ): conv_args = inputs = keras. block_size = block_size def call ( self, input ): batch, height, width, depth = ops. Layer ): def _init_ ( self, block_size ): super ().

Tensorflow upscale image