Data Visualisation for Beginners: How to create a scatter plot in Python
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Shi-Tomasi Corner Detector Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load image image_bgr = cv2.imread(‘images/plane_256x256.jpg’) image_gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) Define Corner Parameters corners_to_detect = 10 minimum_quality_score = 0.05 minimum_distance = 25 Detect Corners corners = cv2.goodFeaturesToTrack(image_gray, corners_to_detect, minimum_quality_score, minimum_distance) corners = np.float32(corners) Mark Corners for corner in corners: x, …
Save Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane.jpg’, cv2.IMREAD_GRAYSCALE) plt.imshow(image, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects for Aspiring Data Scientists: Tabular Text & Image Data Analytics …
Load Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane.jpg’, cv2.IMREAD_GRAYSCALE) plt.imshow(image, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects for Aspiring Data Scientists: Tabular Text & Image Data Analytics …
Binarize Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image_grey = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Apply Adaptive Thresholding max_output_value = 255 neighorhood_size = 99 subtract_from_mean = 10 image_binarized = cv2.adaptiveThreshold(image_grey, max_output_value, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, neighorhood_size, subtract_from_mean) View Image plt.imshow(image_binarized, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% …
Rescale A Feature Preliminaries from sklearn import preprocessing import numpy as np Create Feature x = np.array([[-500.5], [-100.1], [0], [100.1], [900.9]]) Rescale Feature Using Min-Max minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1)) x_scale = minmax_scale.fit_transform(x) x_scale array([[ 0. ], [ 0.28571429], [ 0.35714286], [ 0.42857143], [ 1. ]]) Python Example for Beginners Special 95% discount 2000+ Applied …
Getting The Diagonal Of A Matrix Preliminaries import numpy as np Create Matrix matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) Get The Diagonal matrix.diagonal() array([1, 5, 9]) Calculate The Trace matrix.diagonal().sum() 15 Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects for …
Reshape An Array Preliminaries import numpy as np Create Array matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) Reshape Array matrix.reshape(2, 6) array([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12]]) Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & …
Selecting Elements In An Array Preliminaries # Load library import numpy as np Create Vector # Create row vector vector = np.array([1, 2, 3, 4, 5, 6]) Select Element # Select second element vector[1] 2 Create Matrix # Create matrix matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) Select Element # Select …