keyboard_arrow_down Import necessary libraries !pip install opencv-python !pip install opencv-python-headless Requirement already satisfied: opencv-python in /usr/local/lib/python3.11/dist-packages (4.10.0.84) Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-python) (1.26.4) Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.11/dist-packages (4.11.0.86) Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-python-headless) (1.26.4) from google.colab import files uploaded = files.upload() # After uploading, you can access the image by its filename image_path = list(uploaded.keys())[0] # This will get the name of the uploaded file Choose Files images.jfif images.jfif(image/jpeg) - 4987 bytes, last modified: 1/31/2025 - 100% done Saving images.jfif to images (1).jfif from google.colab import files uploaded = files.upload() # After uploading, you can access the image by its filename image_path = list(uploaded.keys())[0] # This will get the name of the uploaded file Choose Files images.jfif images.jfif(image/jpeg) - 4987 bytes, last modified: 1/31/2025 - 100% done Saving images.jfif to images.jfif import cv2 import numpy as np import requests import urllib # URL of the image url = 'https://example.com/your_image.jpg' # Replace with the actual URL # Read the image from the URL image_response = requests.get(url) image_array = np.asarray(bytearray(image_response.content), dtype=np.uint8) image = cv2.imdecode(image_array, cv2.IMREAD_COLOR) # Now you can use the 'image' variable in your operations from google.colab import files import cv2 import numpy as np import matplotlib.pyplot as plt # Upload the image uploaded = files.upload() image_path = list(uploaded.keys())[0] image = cv2.imread(image_path) # Get the uploaded image filename # The rest of your image processing code goes here... Choose Files images.jfif images.jfif(image/jpeg) - 4987 bytes, last modified: 1/31/2025 - 100% done Saving images.jfif to images (2).jfif # Import necessary libraries import cv2 import numpy as np import matplotlib.pyplot as plt from google.colab import files # Step 1: Upload the image uploaded = files.upload() image_path = list(uploaded.keys())[0] image = cv2.imread(image_path) # Get the uploaded image filename # Function to display images def display_images(images, titles): plt.figure(figsize=(15, 10)) for i in range(len(images)): plt.subplot(2, 3, i + 1) plt.imshow(cv2.cvtColor(images[i], cv2.COLOR_BGR2RGB)) plt.title(titles[i]) plt.axis('off') plt.show() # 1. Sharpening of image def sharpen_image(image): kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]) sharpened = cv2.filter2D(image, -1, kernel) return sharpened sharpened_image = sharpen_image(image) # 2. Rotation def rotate_image(image, angle): (h, w) = image.shape[:2] center = (w // 2, h // 2) M = cv2.getRotationMatrix2D(center, angle, 1.0) rotated = cv2.warpAffine(image, M, (w, h)) return rotated rotated_image = rotate_image(image, 45) # Rotate by 45 degrees # 3. Translation def translate_image(image, tx, ty): M = np.float32([[1, 0, tx], [0, 1, ty]]) translated = cv2.warpAffine(image, M, (image.shape[1], image.shape[0])) return translated translated_image = translate_image(image, 50, 30) # Translate by (50, 30) # 4. Scaling to 2X def scale_image(image, scale): width = int(image.shape[1] * scale) height = int(image.shape[0] * scale) scaled = cv2.resize(image, (width, height), interpolation=cv2.INTER_LINEAR) return scaled scaled_image = scale_image(image, 2) # 5. Mirroring def mirror_image(image): mirrored = cv2.flip(image, 1) return mirrored # Scale to 2X # 1 for horizontal flip mirrored_image = mirror_image(image) # Display all images images = [image, sharpened_image, rotated_image, translated_image, scaled_image, mirrored_image] titles = ['Original Image', 'Sharpened Image', 'Rotated Image', 'Translated Image', 'Scaled Image', 'Mirrored Image'] display_images(images, titles) Choose Files images.jfif images.jfif(image/jpeg) - 4987 bytes, last modified: 1/31/2025 - 100% done Saving images.jfif to images (3).jfif # Save output images cv2.imwrite('sharpened_image.jpg', sharpened_image) cv2.imwrite('rotated_image.jpg', rotated_image) cv2.imwrite('translated_image.jpg', translated_image) cv2.imwrite('scaled_image.jpg', scaled_image) cv2.imwrite('mirrored_image.jpg', mirrored_image) print("Output images saved successfully.") Output images saved successfully.