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Image Augmentation is a very crucial technique in computer vision, used to enhance the sizes and diverse training datasets. OpenCV is a very powerful library for image processing. However, users sometimes encounter issues when trying to augment images of specific sizes, such as 608 x 608 pixels.
In this article, we will look into understanding the problem and try to resolve it in Python.
Why does OpenCV can’t Augment Image 608×608When trying to attempt augmenting images using OpenCV, problems can occur due to these various reasons :
- Incorrect image loading
- Inconsistent image dimensions
- Data Type Mismatches
- Channel misconfiguration
- Memory constraints
Understanding the root cause is essential for effectively addressing and resolving the problem.
Incorrect Image LoadingThis error occurs when the specified image file path is incorrect or the image file does not exist.
Python
# import opencv module
import cv2
# Attempt to load image
image_path = "path_to_non_existing_image.png"
image = cv2.imread(image_path)
# This might fail if the image path is incorrect or the image is corrupted
if image is None:
raise ValueError("Image not found or unable to load")
Output:
ValueError: Image not found or unable to load Incorrect Image DimensionsThis error can occur if the image does not have the expected dimensions, which might happen during loading or processing.
Python
# import opencv module
import cv2
# load image path
image_path = "path_to_image.png"
image = cv2.imread(image_path)
if image is None:
raise ValueError("Image not found or unable to read.")
# Simulate an error where image dimensions are unexpected
if image.shape[:2] != (200, 200):
raise ValueError("Unexpected image dimensions")
Output:
ValueError: Unexpected image dimensions Data Type MismatchThis error occurs when the image has an incorrect data type, which can cause issues during processing.
Python
# import opencv and numpy module
import cv2
import numpy as np
image_path = "path_to_image.png"
# Simulate wrong data type
image = cv2.imread(image_path).astype(np.float32)
if image.dtype != 'uint8':
raise TypeError("Incorrect image data type")
# Convert to correct data type
image = image.astype('uint8')
print(f"Image dtype after correction: {image.dtype}")
Output:
TypeError: Incorrect image data type
Image dtype after correction: uint8 Solution: Convert the image to the correct data type using ‘astype’.
How to fix : OpenCV Image augmentation to 608*608 pixelsWe will explore different methods and techniques to troubleshoot these problems and fix them, ensuring our images are correctly augmented.
Ensure Proper Image Loading In this example, we will verify that the image is correctly loaded from the file. For this we will use OpenCV’s cv2.imread() function which takes the path to the image as a parameter. If the path to image is incorrect or the file is corrupted , OpenCV won’t be able to load it.
Python
import cv2
# Load image from local path
image_path = 'C:\\Users\\Asus\\Dropbox\\PC\\Downloads\\gfg-new-logo.png'
image = cv2.imread(image_path)
if image is None:
raise ValueError("Image not found or unable to load")
print(f"Loaded image shape: {image.shape}")
Output:
Loaded image shape: (400, 1600, 3) Resize the ImageResizing images is a common preprocessing step. OpenCV provides the cv2.resize() function for resizing images. It takes two parameters, first is the image object and the other is the dimensions of the image.
Python
# import opencv module
import cv2
# load image
image_path = "path_to_image.png"
image = cv2.imread(image_path)
# resize image
resized_image = cv2.resize(image, (608, 608))
print(f"Resized image shape: {resized_image.shape}")
Output:
Resized image shape: (608, 608, 3) Maintain Accept Ratio (Padding)Resizing an image to a fixed size without maintaining the aspect ratio can distort the image. To avoid this, you can resize the image while maintaining the aspect ratio and then pad the image to the desired size. The cv2.copyMakeBorder() function adds borders to the resized image to achieve the target size.
Python
# import opencv module
import cv2
# load image
image_path = "path_to_image.png"
image = cv2.imread(image_path)
# function to resize image
# maintaing the aspect ratio
def resize_with_aspect_ratio(image, target_size):
h, w = image.shape[:2]
scale = min(target_size / h, target_size / w)
new_w = int(w * scale)
new_h = int(h * scale)
resized_image = cv2.resize(image, (new_w, new_h))
delta_w = target_size - new_w
delta_h = target_size - new_h
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [0, 0, 0]
new_image = cv2.copyMakeBorder(
resized_image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
return new_image
resized_image = resize_with_aspect_ratio(image, 608)
print(f"Resized image shape: {resized_image.shape}")
Output:
Resized image shape: (608, 608, 3) Verify Data Types and ChannelsIt is important to ensure that image has the correct data type and number of channels ( usually ‘uint8’ and 3 channels for RGB)
Python
# import opencv module
import cv2
# load image
image_path = "path_to_image.png"
image = cv2.imread(image_path)
# resize image
resized_image = cv2.resize(image, (608, 608))
print(f"Resized image shape: {resized_image.shape}")
# varify datatypes and channels
print(f"Image dtype: {resized_image.dtype}")
print(f"Number of channels: {resized_image.shape[2]}")
Output:
Resized image shape: (608, 608, 3)
Image dtype: uint8
Number of channels: 3 Perform Augmentation OpenCV offers various augmentation techniques, including rotation, flipping, and color adjustments. Properly implementing these techniques can help avoid issues with specific image sizes. Let us see a few of them.
Rotation
In this example, we will rotate the image by a specified angle of45 degrees. For this purpose, we will use the cv2.getRotationMatrix2D() function to create a rotation matrix and the cv2.warpAffine() function to apply the rotation matrix to the image.
Python
# import opencv module
import cv2
# load image
image_path = "path_to_image.png"
image = cv2.imread(image_path)
# resize image
resized_image = cv2.resize(image, (608, 608))
# function to rotate image
def rotate_image(image, angle):
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
# creating rotation matrix
M = cv2.getRotationMatrix2D(center, angle, 1.0)
# applying rotation to the image
rotated = cv2.warpAffine(image, M, (w, h))
return rotated
rotated_image = rotate_image(resized_image, 45)
# displaying image
cv2.imshow("Rotated Image", rotated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
Rotated image shape: (608, 608, 3) Flipping
In this example, we will flip the image horizontally. To do so, we will use the cv2.flip() function to flip the image along the specified axis (1 for horizontal flip).
Python
# import opencv module
import cv2
# load image
image_path = "path_to_image.png"
image = cv2.imread(image_path)
# resize image
resized_image = cv2.resize(image, (608, 608))
# Horizontal flip
flipped_image = cv2.flip(image_resized, 1)
print(f"Flipped image shape: {flipped_image.shape}")
cv2.imshow("Flipped Image", flipped_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
Flipped image shape: (608, 608, 3) Translation
In this example, we will translates the image, that is, shift image by specified x and y values. In this case by 100 pixels right and 50 pixels down. This can be achieved by the cv2.warpAffine() function to apply the translation matrix to the image.
Python
# import opencv and numpy module
import cv2
import numpy as np
# load image
image_path = "path_to_image.png"
image = cv2.imread(image_path)
# resize image
resized_image = cv2.resize(image, (608, 608))
# funtion to translate image
def translate_image(image, x, y):
M = np.float32([[1, 0, x], [0, 1, y]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
return shifted
translated_image = translate_image(resized_image, 100, 50)
print(f"Translated image shape: {translated_image.shape}")
# displaying image
cv2.imshow("Rotated Image", translated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Output:
Translated image shape: (608, 608, 3) The first image is the original image, second is the rotated image, third image is the flipped and the forth as we can see the translated image.
 performance agumentation ConclusionBy following these methods , we can easly resolve common issues related to image augmentation using OpenCV . Proper image loading , resizing , managing the accept ratios , verifying data types and channels , performing augmentations , and thorough debugging will help ensure sucessful image processing .
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