Bag Of Words Preliminaries import numpy as np from sklearn.feature_extraction.text import CountVectorizer import pandas as pd Create Text Data text_data = np.array([‘I love Brazil. Brazil!’, ‘Sweden is best’, ‘Germany beats both’]) Create Bag Of Words count = CountVectorizer() bag_of_words = count.fit_transform(text_data) bag_of_words.toarray() array([[0, 0, 0, 2, 0, 0, 1, 0], [0, 1, 0, 0, 0, …
Day: May 21, 2021
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 …
Enhance Contrast Of Greyscale Image Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Enhance Image image_enhanced = cv2.equalizeHist(image) View Image plt.imshow(image_enhanced, cmap=’gray’), plt.axis(“off”) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects …
Detect Edges Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load image image_gray = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Detect Edges median_intensity = np.median(image_gray) lower_threshold = int(max(0, (1.0 – 0.33) * median_intensity)) upper_threshold = int(min(255, (1.0 + 0.33) * median_intensity)) image_canny = cv2.Canny(image_gray, lower_threshold, upper_threshold) View Edges plt.imshow(image_canny, cmap=’gray’), plt.axis(“off”) plt.show() …
Blurring Images Preliminaries import cv2 import numpy as np from matplotlib import pyplot as plt Load Image As Greyscale image = cv2.imread(‘images/plane_256x256.jpg’, cv2.IMREAD_GRAYSCALE) Blur Image image_blurry = cv2.blur(image, (5,5)) View Image plt.imshow(image_blurry, cmap=’gray’), plt.xticks([]), plt.yticks([]) plt.show() Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio Projects for …
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% …
Standardize 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]]) Standardize Feature scaler = preprocessing.StandardScaler() standardized = scaler.fit_transform(x) standardized array([[-1.26687088], [-0.39316683], [-0.17474081], [ 0.0436852 ], [ 1.79109332]]) Python Example for Beginners Special 95% discount 2000+ Applied Machine Learning & Data Science Recipes Portfolio …
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 …