WebTo solve the error, make sure to return a value from the function. main.py. import cv2 def get_path(): return 'thumbnail.webp' # 👇️ None img = cv2.imread(get_path()) print(img.shape) # 👉️ (120, 632, 3) We used the return statement to return a string from the get_path function, so everything works as expected. WebImage thumbnails In addition to our border-radius utilities , you can use .img-thumbnail to give an image a rounded 1px border appearance. A generic square placeholder image with a white border around it, making it resemble a photograph taken with an old instant camera

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WebReturns a tensor containing the shape of the input tensor. WebJan 31, 2024 · Then the shape of the object holds a tuple (rows, columns, channels). (height,width)=img.shape [:2] is an example of tuple unpacking, with it you extract the … finglen house and cabins

How to Build a Convolutional Neural Network in Python with Keras …

Webimg = Input(shape=(self.img_rows, self.img_cols, 3)) # Mean center and rescale by variance as in PyTorch: processed = Lambda(lambda x: (x-self.mean) / self.std)(img) # If inference only, just return empty model : if self.inference_only: model = Model(inputs=img, outputs=[img for _ in range(len(self.vgg_layers))]) model.trainable = False Weband that ':' is used as a wildcard.. but not sure how this works: ( H , W ) = image.shape [:2] It's a feature of python they call "slicing", Google it. This is a combination of slicing and sequence unpacking. The colon isn't a wildcard; it denotes a range, or more precisely a slice of the array's indices. It's meant to be intuitive and the ... Webimg_A = Input(shape=self.img_shape) img_B = Input(shape=self.img_shape) # By conditioning on B generate a fake version of A: fake_A = self.generator(img_B) # For the combined model we will only train the generator: self.discriminator.trainable = False # Discriminators determines validity of translated images / condition pairs finglenut