【PYTHON OPENCV】Understanding cv2.dnn.blobFromImages() and cv2.dnn.imagesFromBlob() in OpenCV with cropping

 """

Understanding cv2.dnn.blobFromImages() and cv2.dnn.imagesFromBlob() in OpenCV with cropping """ # Import required packages: import cv2 import numpy as np from matplotlib import pyplot as plt def get_cropped_img(img): """Returns the cropped image""" # Create a copy of the image: img_copy = img.copy() # calculate size of resulting image: size = min(img_copy.shape[1], img_copy.shape[0]) # calculate x1, and y1 x1 = int(0.5 * (img_copy.shape[1] - size)) y1 = int(0.5 * (img_copy.shape[0] - size)) # crop and return the image return img_copy[y1:(y1 + size), x1:(x1 + size)] def show_img_with_matplotlib(color_img, title, pos): """Shows an image using matplotlib capabilities""" img_RGB = color_img[:, :, ::-1] ax = plt.subplot(2, 2, pos) plt.imshow(img_RGB) plt.title(title) plt.axis('off') def get_images_from_blob(blob_imgs, scalefactor, dim, mean, swap_rb, mean_added): """Returns images from blob""" images_from_blob = cv2.dnn.imagesFromBlob(blob_imgs) imgs = [] for image_blob in images_from_blob: image_from_blob = np.reshape(image_blob, dim) / scalefactor image_from_blob_mean = np.uint8(image_from_blob) image_from_blob = image_from_blob_mean + np.uint8(mean) if mean_added is True: if swap_rb: image_from_blob = image_from_blob[:, :, ::-1] imgs.append(image_from_blob) else: if swap_rb: image_from_blob_mean = image_from_blob_mean[:, :, ::-1] imgs.append(image_from_blob_mean) return imgs # Load images and get the list of images: image = cv2.imread("face_test.png") image2 = cv2.imread("face_test2.jpg") images = [image, image2] # To see how cropping works, we are going to perform the cropping formulation that # both blobFromImage() and blobFromImages() perform applying it to one of the input images: cropped_img = get_cropped_img(image) # cv2.imwrite("cropped_img.jpg", cropped_img) # Call cv2.dnn.blobFromImages(): blob_images = cv2.dnn.blobFromImages(images, 1.0, (300, 300), [104., 117., 123.], False, False) blob_blob_images_cropped = cv2.dnn.blobFromImages(images, 1.0, (300, 300), [104., 117., 123.], False, True) # Get different images from the blob: imgs_from_blob = get_images_from_blob(blob_images, 1.0, (300, 300, 3), [104., 117., 123.], False, True) imgs_from_blob_cropped = get_images_from_blob(blob_blob_images_cropped, 1.0, (300, 300, 3), [104., 117., 123.], False, True) # Create the dimensions of the figure and set title: fig = plt.figure(figsize=(10, 8)) plt.suptitle("cv2.dnn.blobFromImages() visualization with cropping", fontsize=14, fontweight='bold') fig.patch.set_facecolor('silver') # Show the input images show_img_with_matplotlib(imgs_from_blob[0], "img 1 from blob " + str(imgs_from_blob[0].shape), 1) show_img_with_matplotlib(imgs_from_blob[1], "img 2 from blob " + str(imgs_from_blob[1].shape), 2) show_img_with_matplotlib(imgs_from_blob_cropped[0], "img 1 from blob cropped " + str(imgs_from_blob[1].shape), 3) show_img_with_matplotlib(imgs_from_blob_cropped[1], "img 2 from blob cropped " + str(imgs_from_blob[1].shape), 4) # Show the Figure: plt.show()