黒板とかホワイトボードに投影した映像をカメラで映して何かしようとした残骸。

automapping.py

import time

import cv2
import numpy


SCREEN_SIZE = (1024, 768)


cam = cv2.VideoCapture(0)

cv2.namedWindow('screen', 0x10)


def take_photo():
    return cv2.resize(cam.read()[1], (1024, 768))


def take_clear_photo():
    img = numpy.array(take_photo(), numpy.uint64)
    for i in range(11):
        time.sleep(0.01)
        img += take_photo()
    return numpy.array(img/12, numpy.uint8)


def take_gray_photo():
    return cv2.cvtColor(take_clear_photo(), cv2.COLOR_BGR2GRAY)


def find_max_pos(img):
    max_score = 0
    max_pos = (0, 0)

    for y in range(0, img.shape[0], 8):
        for x in range(0, img.shape[1], 8):
            score = img[y:y+8, x:x+8].sum()

            if score > max_score:
                max_score = score
                max_pos = (x + 4, y + 4)

    return max_pos


def find_diff_pos(a, b):
    return find_max_pos(((cv2.absdiff(a, b) > 128) * 255).astype(numpy.uint8))


def display(img, name='screen'):
    cv2.imshow(name, img)
    cv2.waitKey(1)


def find_corner():
    poses = []

    for x, y in ((0, 0),
                 (SCREEN_SIZE[0], 0),
                 SCREEN_SIZE,
                 (0, SCREEN_SIZE[1])):

        img = numpy.zeros(SCREEN_SIZE[::-1], numpy.uint8)
        display(img)

        time.sleep(0.1)
        bg = take_gray_photo()

        cv2.circle(img, (x, y), 32, 255, -1)
        display(img)

        time.sleep(0.1)
        img = take_gray_photo()

        poses.append(find_diff_pos(bg, img))

    corners = ((0, 0),
               (img.shape[1], 0),
               (img.shape[1], img.shape[0]),
               (0, img.shape[0]))

    m = cv2.getPerspectiveTransform(numpy.array(poses, numpy.float32),
                                    numpy.array(corners, numpy.float32))

    return poses, m


class ContourDetector:
    def __init__(self, initial_img):
        self.lowpass = initial_img

    def update(self, img):
        self.lowpass = (self.lowpass*0.9 + img*0.1).astype(numpy.uint8)

        hsv = cv2.GaussianBlur(self.lowpass, (9, 9), 10)
        hsv = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV)

        filtered = (
            (100 < hsv[:,:,0])
            * (hsv[:,:,0] < 110)
            * 255
        ).astype(numpy.uint8)

        binary = cv2.GaussianBlur(filtered, (9, 9), 10)
        display(binary, name='binary')
        binary = cv2.threshold(binary, 128, 255, cv2.THRESH_BINARY)[1]

        cnts = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
        lines = img.copy()
        for c in cnts:
            arclen = cv2.arcLength(c, True)
            approx = cv2.approxPolyDP(c, 0.02*arclen, True)
            cv2.drawContours(lines, [approx], -1, (0, 255, 0), -1)

        return cnts, lines


if __name__ == '__main__':
    corner, m = find_corner()

    cd = ContourDetector(take_photo())

    while True:
        img = take_photo()

        warped = cv2.warpPerspective(img, m, (img.shape[1], img.shape[0]))
        display(cd.update(warped)[1])

        for c in corner:
            cv2.circle(img, c, 4, (0, 255, 0), -1)
            cv2.circle(img, c, 6, (0, 255, 0), 1)
        display(img, name='monitor')

        k = cv2.waitKey(15)
        if k == 27:
            break
        elif k == ord(' '):
            corner, m = find_corner()

color_filtering.py

import cv2
import numpy


cam = cv2.VideoCapture(0)
lowpass = cam.read()[1]

while cv2.waitKey(15) < 0:
    ok, img = cam.read()
    if not ok:
        break

    lowpass = numpy.array(lowpass*0.9 + img*0.1, numpy.uint8)

    hsv = cv2.GaussianBlur(lowpass, (9, 9), 10)

    hsv = cv2.cvtColor(hsv, cv2.COLOR_BGR2HSV)

    filtered = numpy.array((100 < hsv[:,:,0]) * (hsv[:,:,0] < 110) * 255, numpy.uint8)

    binary = cv2.GaussianBlur(filtered, (9, 9), 10)

    binary = cv2.threshold(binary, 128, 255, cv2.THRESH_BINARY)[1]
    cv2.imshow('binary', binary)

    cnts = cv2.findContours(binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
    lines = img.copy()
    for c in cnts:
        arclen = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.02*arclen, True)
        cv2.drawContours(lines, [approx], -1, (0, 255, 0), -1)

    cv2.imshow('lines', lines)
    cv2.imshow('filtered', filtered)