91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
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#!/usr/bin/python3
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# https://pyimagesearch.com/2018/07/19/opencv-tutorial-a-guide-to-learn-opencv/
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# import the necessary packages
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import cv2
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import imutils
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import numpy as np
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import argparse
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def rescale_frame(frame, percent): # rescale image considering aspect ratio
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width = int(frame.shape[1] * percent/ 100)
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height = int(frame.shape[0] * percent/ 100)
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dim = (width, height)
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return cv2.resize(frame, dim, interpolation =cv2.INTER_AREA)
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# alternative: resized = imutils.resize(image, width=300) => give goal height or width
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def extract_channels(image,color_space):
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(ch1,ch2,ch3) = cv2.split(image)
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cv2.imshow("Channel "+color_space[0],ch1)
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cv2.imshow("Channel "+color_space[1],ch2)
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cv2.imshow("Channel "+color_space[2],ch3)
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def rotate_bound(image, angle):
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(h, w) = image.shape[:2] # image dimesnsions
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(cX, cY) = (w // 2, h // 2) # center of image
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M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0) # neg angle to rotate clockwise
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cos = np.abs(M[0, 0]) # cos & sin value from rotation matrix
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sin = np.abs(M[0, 1])
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# compute the new bounding dimensions of the image
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nW = int((h * sin) + (w * cos))
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nH = int((h * cos) + (w * sin))
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# adjust the rotation matrix to take into account translation
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M[0, 2] += (nW / 2) - cX
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M[1, 2] += (nH / 2) - cY
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# perform the actual rotation and return the image
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return cv2.warpAffine(image, M, (nW, nH))
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# load the input image and show its dimensions, keeping in mind that
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# images are represented as a multi-dimensional NumPy array with
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# shape no. rows (height) x no. columns (width) x no. channels (depth)
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image = cv2.imread('/home/caroline/Pictures/first.jpg') #numpy array
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# resize image ignoring aspect ratio
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resized_image = cv2.resize(image, (300, 300))
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cv2.imshow("Fixed Resizing", resized_image)
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cv2.waitKey(0)
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image = rescale_frame(image,20)
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(h, w, d) = image.shape #height=rows, width=columns, depth=channels => 3 for RGB
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print("width={}, height={}, depth={}".format(w, h, d))
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# display the image to our screen -- we will need to click the window
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# open by OpenCV and press a key on our keyboard to continue execution
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cv2.imshow("Image", image)
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cv2.waitKey(0)
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# grayscale value 0-255
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# (B, G, R) with range [0, 255]
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# access the RGB pixel located at x=50, y=100, keepind in mind that
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# OpenCV stores images in BGR order rather than RGB
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(B, G, R) = image[100, 50]
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print("R={}, G={}, B={}".format(R, G, B))
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# array slicing and cropping
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# extract a 100x100 pixel square ROI (Region of Interest) from the
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# input image starting at x=320,y=60 at ending at x=420,y=160
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""" roi = image[60:160, 320:420]
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cv2.imshow("ROI", roi)
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cv2.waitKey(0) """
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# transform image into different color spaces and split channels
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""" image_lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
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extract_channels(image_lab,"LAB") # lightness, red/green value, blue/yellow value => very vast color space
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cv2.waitKey(0)
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image_hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # hue:color 0°-360°, saturation: amount of grey 0-1, value: brightness/intensity 0-1
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extract_channels(image_hsv,"HSV")
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cv2.waitKey(0) """
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# rotate image
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# loop over rotation angles without cutting image
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""" for angle in np.arange(0, 360, 30): # range [0, 360] in 15° increments
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rotated = rotate_bound(image, angle)
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cv2.imshow("Rotated", rotated)
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cv2.waitKey(0) """
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# blurring: reduce high-frequency noise => gaussianBlur function
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blurred = cv2.GaussianBlur(image, (11, 11), 0) # 11x11 kernel: larger kernel create more blurry images
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cv2.imshow("Blurred", blurred)
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cv2.waitKey(0)
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