![]() 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) # read the example image used for tracing Here, our focus will be on how to use the trackbar function in OpenCV to create a custom callback function. Model = ("traced_facelearner_model_new.pt") You have already learned to resize images in OpenCV, to review the details again, please take a look at this post. 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. Return cv2.resize(image, dim, interpolation=inter)Ĭv2.imshow('width_100', maintain_aspect_ratio_resize(image, width=100))Ĭv2.The CNN model takes an image tensor of size (112x112) as input and gives (1x512) size tensor as output. # Calculate the ratio of the width and construct the dimensions # We are resizing width if height is none # Calculate the ratio of the height and construct the dimensions # We are resizing height if width is none # Return original image if no need to resize # Grab the image size and initialize dimensions Here's a function to upscale or downscale an image by desired width or height while maintaining aspect ratio # Resizes a image and maintains aspect ratioĭef maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA): Thumbnail = cv.resize(im, (round(width / 10), round(height / 10)), interpolation=cv.INTER_AREA) Using your code with cv2 import cv2 as cv Img = cv2.resize(img, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA) Scaling_factor = max_width / float(width) If max_width/float(width) < scaling_factor: Scaling_factor = max_height / float(height) If max_height < height or max_width < width: You can resize an input image with either of following methods: import numpy as np import cv2 as cv img cv. ![]() ![]() # only shrink if img is bigger than required By default, the interpolation method cv.INTERLINEAR is used for all resizing purposes. Example shrink image to fit a max height/width (keeping aspect ratio) import cv2 resize cv2.resize (image, (x-axis dimensions, y-axis dimensions)) Step 5.3: Saving the frames with certain names Later on, we save the image frames we resize in the last step. To shrink an image, it will generally look best with INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with INTER_CUBIC (slow) or INTER_LINEAR (faster but still looks OK). In this step, we have used resize () function with particular dimensions for which the image frames need to be set. Where fx is the scaling factor along the horizontal axis and fy along the vertical axis. The new size can be specified:ĭst = cv2.resize(src, (2*width, 2*height), interpolation = cv2.INTER_CUBIC)ĭst = cv2.resize(src, None, fx = 2, fy = 2, interpolation = cv2.INTER_CUBIC), Reading an Image using OpenCV imread () function Image resizing with a custom Width and Height Resizing an image with a Scaling factor Image resizing with different Interpolation methods Summary Let’s go through the code example for making an image larger and smaller by resizing with custom height and width.
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