Object tracking using Homography – OpenCV 3.4 with python 3 Tutorial 34
Source code: import cv2 import numpy as np img = cv2.imread("ultimo_sopravvissuto.jpg", cv2.IMREAD_GRAYSCALE) # queryiamge cap = cv2.VideoCapture(0) # Features sift = cv2.xfeatures2d.SIFT_create() kp_image, desc_image = sift.detectAndCompute(img, None) # Feature matching index_params = dict(algorithm=0, trees=5) search_params = dict() flann = cv2.FlannBasedMatcher(index_params, search_params) while True: _,…
k-Nearest Neighbour classification – OpenCV 3.4 with python 3 Tutorial 33
Source code: import cv2 import numpy as np def mouse_pos(event, x, y, flags, params): global squares, color, new_element if event == cv2.EVENT_LBUTTONDOWN: if color == "b": blue_squares.append([x, y]) elif color == "r": red_squares.append([x, y]) else: new_element = [x, y] # Create Window and Set mouse…
Background Subtraction – OpenCV 3.4 with python 3 Tutorial 32
We’re going to learn in this tutorial how to subtract the background on a video. The concept of background subtraction is really simple. On the video we take the first frame, and we find the absolute difference with another frame. Let’s see an example where…
Optical Flow with Lucas-Kanade method – OpenCV 3.4 with python 3 Tutorial 31
Source code: import cv2 import numpy as np cap = cv2.VideoCapture(0) # Create old frame _, frame = cap.read() old_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Lucas kanade params lk_params = dict(winSize = (15, 15), maxLevel = 4, criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) # Mouse…
Object tracking with Camshift – OpenCV 3.4 with python 3 Tutorial 30
Source code: import cv2 import numpy as np img = cv2.imread("gray_cover.jpg") roi = img[252: 395, 354: 455] x = 354 y = 252 width = 455 – x height = 395 – y hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) roi_hist = cv2.calcHist([hsv_roi], [0], None, [180], [0,…
Object tracking with Mean-shift – OpenCV 3.4 with python 3 Tutorial 29
Source code: import cv2 import numpy as np video = cv2.VideoCapture("mouthwash.avi") _, first_frame = video.read() x = 300 y = 305 width = 100 height = 115 roi = first_frame[y: y + height, x: x + width] hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) roi_hist = cv2.calcHist([hsv_roi],…
Histogram and Back Projection – OpenCV 3.4 with python 3 Tutorial 28
import cv2 import numpy as np from matplotlib import pyplot as plt original_image = cv2.imread("goalkeeper.jpg") hsv_original = cv2.cvtColor(original_image, cv2.COLOR_BGR2HSV) roi = cv2.imread("pitch_ground.jpg") hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) hue, saturation, value = cv2.split(hsv_roi) # Histogram ROI roi_hist = cv2.calcHist([hsv_roi], [0, 1], None, [180, 256], [0, 180, 0,…
Mouse Events – OpenCV 3.4 with python 3 Tutorial 27
Draw circles: import cv2 import numpy as np def mouse_drawing(event, x, y, flags, params): if event == cv2.EVENT_LBUTTONDOWN: print("Left click") circles.append((x, y)) cap = cv2.VideoCapture(0) cv2.namedWindow("Frame") cv2.setMouseCallback("Frame", mouse_drawing) circles = [] while True: _, frame = cap.read() for center_position in circles: cv2.circle(frame, center_position, 5,…
Feature Matching (Brute-Force) – OpenCV 3.4 with python 3 Tutorial 26
Source code: import cv2 import numpy as np img1 = cv2.imread("the_book_thief.jpg", cv2.IMREAD_GRAYSCALE) img2 = cv2.imread("me_holding_book.jpg", cv2.IMREAD_GRAYSCALE) # ORB Detector orb = cv2.ORB_create() kp1, des1 = orb.detectAndCompute(img1, None) kp2, des2 = orb.detectAndCompute(img2, None) # Brute Force Matching bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) matches = bf.match(des1, des2) matches…
Feature detection (SIFT, SURF, ORB) – OpenCV 3.4 with python 3 Tutorial 25
We’re going to learn in this tutorial how to find features on an image.We have thre different algorythms that we can use: SIFT SURF ORB Each one of them as pros and cons, it depends on the type of images some algorithm will detect more…
