![]() The output1 and output2 tensors have different values. Tensor2 = test_transform2(omarray(image)).to(device).unsqueeze(0) Tensor1 = test_transform(resized_image).to(device).unsqueeze(0) Resized_image = cv2.resize(image, (112, 112)) image32 cv2.imread(image).astype(np.float32) cv2.cvtColor(image32,cv2.COLORBGR2RGB,image32) newimage cv2.resize(image32, (80,80),interpolationcv2.INTERLINEAR) Upper left pixel value is: 145.91113, 76.853516, 92. OpenCV provides a function called resize to achieve image scaling. # read the example image used for tracing Model = ("traced_facelearner_model_new.pt") What's the reason for this? (I understand that the difference in the underlying implementation of opencv resizing vs torch resizing might be a cause for this, But I'd like to have a detailed understanding of it) import cv2įrom torchvision import transforms as trans Using Opencv function cv2.resize() or using Transform.resize in pytorch to resize the input to (112x112) gives different outputs. ![]() Syntax: cv2. For example, this will resize both axes by half: small cv2.resize(image, (0,0), fx0.5. This is the default interpolation technique in OpenCV. If you wish to use CV2, you need to use the resize function. cv2.INTERLINEAR: This is primarily used when zooming is required. cv2.INTERCUBIC: This is slow but more efficient. For example import cv2 from PIL import Image import numpy as np a cv2.imread ('videos/example.jpg') b cv2.resize (a, (112, 112)) c np.array (omarray (a).resize ( (112, 112), Image.BILINEAR)) You will see that b and c are slightly different. There are several ways to resize the image.The CNN model takes an image tensor of size (112x112) as input and gives (1x512) size tensor as output. Choice of Interpolation Method for Resizing: cv2.INTERAREA: This is used when we need to shrink an image. While in your code you simply use cv2.resize which doesn't use any interpolation.
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