feature-extraction

问题 I trying to implement a Mel Spectrogram feature extraction: n_mels = 128 # Extracting MelFrequency Spectrum for every file def extract_features(file_name): try: audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast') mely = librosa.feature.melspectrogram(y=audio, sr=sample_rate, n_mels=n_mels) except Exception as e: print("Error encountered while parsing file: ", file) return None return mely.T It appears that I am implementing this feature extraction incorrectly as when I check

问题 I trying to implement a Mel Spectrogram feature extraction: n_mels = 128 # Extracting MelFrequency Spectrum for every file def extract_features(file_name): try: audio, sample_rate = librosa.load(file_name, res_type='kaiser_fast') mely = librosa.feature.melspectrogram(y=audio, sr=sample_rate, n_mels=n_mels) except Exception as e: print("Error encountered while parsing file: ", file) return None return mely.T It appears that I am implementing this feature extraction incorrectly as when I check

问题 I have a feature array which is created from ORB with this code part: orb = cv2.ORB_create() #keypoints and descriptors kpO, desO = orb.detectAndCompute(gray_image, None) I create this gray image from colored one. And des0.shape is (500,32). Also shape of my original image(colored one) is (422, 750, 3). And I want to decisionTreeRegression from these arrays for predict the colored version of grayscale image which is I created from colored version. But the problem is starting here , feature

问题 I have two different types of images (camera image and it's corresponding sketch). The goal of the network is to find the similarity between both images. The network consists of a single encoder and a single decoder. The motivation behind the single encoder-decoder is to share the weights between them. input_img = Input(shape=(img_width,img_height, channels)) def encoder(input_img): # Photo-Encoder Code pe = Conv2D(96, kernel_size=11, strides=(4,4), padding = 'SAME')(left_input) # (?, 64, 64,

问题 I have two different types of images (camera image and it's corresponding sketch). The goal of the network is to find the similarity between both images. The network consists of a single encoder and a single decoder. The motivation behind the single encoder-decoder is to share the weights between them. input_img = Input(shape=(img_width,img_height, channels)) def encoder(input_img): # Photo-Encoder Code pe = Conv2D(96, kernel_size=11, strides=(4,4), padding = 'SAME')(left_input) # (?, 64, 64,

问题 I have run the face detection algorithm inbuilt in opencv to extract faces in each frame of a video(sampled at 1 fps). I have also resized each face image to be of same size and I have cropped some fraction of image to remove background noise and hair. Now the problem is that I have to cluster these images of faces - Each cluster corresponding to a person. I implemented the algorithm described here http://bitsearch.blogspot.in/2013/02/unsupervised-face-clustering-with-opencv.html Basically

问题 I have run the face detection algorithm inbuilt in opencv to extract faces in each frame of a video(sampled at 1 fps). I have also resized each face image to be of same size and I have cropped some fraction of image to remove background noise and hair. Now the problem is that I have to cluster these images of faces - Each cluster corresponding to a person. I implemented the algorithm described here http://bitsearch.blogspot.in/2013/02/unsupervised-face-clustering-with-opencv.html Basically

问题 I am attempting to understand more about computer vision models, and I'm trying to do some exploring of how they work. In an attempt to understand how to interpret feature vectors more I'm trying to use Pytorch to extract a feature vector. Below is my code that I've pieced together from various places. import torch import torch.nn as nn import torchvision.models as models import torchvision.transforms as transforms from torch.autograd import Variable from PIL import Image img=Image.open(

问题 I am attempting to understand more about computer vision models, and I'm trying to do some exploring of how they work. In an attempt to understand how to interpret feature vectors more I'm trying to use Pytorch to extract a feature vector. Below is my code that I've pieced together from various places. import torch import torch.nn as nn import torchvision.models as models import torchvision.transforms as transforms from torch.autograd import Variable from PIL import Image img=Image.open(

来源： https://stackoverflow.com/questions/64348650/use-the-column-of-a-dataframe-that-has-a-list-of-dictionaries-to-create-other-co