Gentle Intro to OpenCV

FeatureDetection.py

import cv2
import numpy as np

def magnitude(image, pos):
    x, y = pos
    return np.mean(image[x, y])

def isPeak(image, pos, threshold):
    x, y = pos
    mag = magnitude(image, pos)
    for i in range(x-1, x+1):
        for j in range(y-1, y+1):
            if i == x and j == y:
                continue
            elif (magnitude(image, (i, j)) / mag) > (1 - threshold) :
                return False
    return True

def feature_detector(image, threshold):
    features = []
    for i in range(1, image.shape[0]-1):
        for j in range(1, image.shape[1]-1):
            if isPeak(image, (i, j), threshold):
                features.append((i, j))
    return features

image = cv2.imread('./small.tiff')
features = feature_detector(image, 0.1)

for (y, x) in features:
    image = cv2.circle(image, (x, y), 2, (0, 0, 0), 0)

cv2.imshow('features', image)
k = cv2.waitKey(0)

Step1a.py

import cv2

image = cv2.imread('./4.2.04.tiff')
cv2.imshow('Hello World', image)
k = cv2.waitKey(0)

Step1b.py

import cv2

image = cv2.imread('./4.2.04.tiff')
cv2.imshow('Hello World', image)
#k = cv2.waitKey(0)
# Nothing happens!

Step2a.py

import cv2

image = cv2.imread('./4.2.04.tiff')
# Why does this remove the Blue channel? 
image[:, :, 0] = 0
cv2.imshow('Hello World', image)
k = cv2.waitKey(0)

Step2b.py

import cv2

image = cv2.imread('./4.2.04.tiff')
# Since OpenCV uses BGR, this code will correctly remove the red channel
image[:, :, 2] = 0
cv2.imshow('Hello World', image)
k = cv2.waitKey(0)

Step2c.py

import cv2

image = cv2.imread('./4.2.04.tiff')

new_image = np.zeros_like(image)
new_image[0:256, 0:256] = image[256:512, 0:256]
new_image[256:512, 0:256] = image[256:512, 256:512]
new_image[256:512, 256:512] = image[0:256, 256:512]
new_image[0:256, 256:512] = image[0:256, 0:256]

cv2.imshow('Hello World', new_image)
k = cv2.waitKey(0)

Step3.py

import cv2

image = cv2.imread('./4.2.04.tiff')

# OpenCV automatically converts to the correct output format
cv2.imwrite('out.png', image)

Step4a.py

import cv2

image = np.zeros([400, 400, 3])
image = cv2.circle(image, (0, 0), 50, (255, 0, 0), 1)
image = cv2.circle(image, (200, 200), 50, (0, 255, 0), 1)
image = cv2.circle(image, (400, 400), 50, (0, 0, 255), 1)
cv2.imshow('Hello World', image)
k = cv2.waitKey(0)

Step4b.py

import cv2

image = np.zeros([400, 400, 3])

for i in range(20):
    pos = tuple(np.random.random_integers(400, size=2))
    radius = np.random.random_integers(5, 50)
    # If using a numpy array, OpenCV expects values from 0 to 1.0
    color = np.random.uniform(size=3)
    image = cv2.circle(image, pos, radius, color, 1)

cv2.imshow('Hello World', image)
k = cv2.waitKey(0)

Step4c.py

import cv2

image = np.zeros([400, 400, 3])

for i in range(20):
    pos1 = tuple(np.random.random_integers(400, size=2))
    pos2 = tuple(np.random.random_integers(400, size=2))
    # If using a numpy array, OpenCV expects values from 0 to 1.0
    color = np.random.uniform(size=3)
    image = cv2.line(image, pos1, pos2, color, 1)

cv2.imshow('Hello World', image)
k = cv2.waitKey(0)