我一直在使用https://gist.github.com/chsasank/4d8f68caf01f041a6453e67fb30f8f5a进行图像处理。这需要一个只有一个通道的图像（参见<canvas id="canvas" width=500 height=500 style="display: block; border: 1px solid green; margin: auto;"></canvas>
<script>
var ctx = document.getElementById('canvas').getContext('2d');
ctx.font = '30px Arial';
var HEIGHT = 500;
var WIDTH = 500;
var SEGMENT_WIDTH = 30;
var snakeVelocity = {
i: 1,
j: 0
};
var snakeArray = createSnake();
function createSnake() {
var snakeArray = [];
var length = 5; // Initial length of snake
for (var i = 0; i < length; i++) {
snakeArray.push({
x: i + 1,
y: 1
});
}
return snakeArray;
}
function moveSnake(arr) {
var head = arr.slice(-1)[0];
var tail = arr[0];
var newHead = arr.shift();
// check for wall collision, which also updates velocity if needed
snakeWallCollision(head);
newHead.x = head.x + snakeVelocity.i;
newHead.y = head.y + + snakeVelocity.j;
arr.push(newHead);
return arr;
}
function snakeWallCollision(obj) {
var collision = false;
if (obj.x >= WIDTH / SEGMENT_WIDTH || obj.x <= 0) {
snakeVelocity.i *= -1;
collision = true;
}
if (obj.y >= HEIGHT / SEGMENT_WIDTH || obj.y <= 0) {
snakeVelocity.j *= -1;
collision = true;
}
return collision;
}
function drawSnake() {
console.log(snakeArray[0]);
for (var i = 0; i < snakeArray.length; i++) {
var segment = snakeArray[i];
ctx.fillText('S', segment.x * SEGMENT_WIDTH, segment.y * SEGMENT_WIDTH + 30);
}
}
function update() {
ctx.clearRect(0, 0, WIDTH, HEIGHT);
moveSnake(snakeArray);
drawSnake();
}
function checkKey(e) {
e = e || window.event;
if ([38, 40, 37, 39].includes(e.keyCode)) {
e.preventDefault();
}
if (e.keyCode == '38') {
snakeVelocity = {
i: 0,
j: -1
};
} else if (e.keyCode == '40') {
snakeVelocity = {
i: 0,
j: 1
};
} else if (e.keyCode == '37') {
snakeVelocity = {
i: -1,
j: 0
};
} else if (e.keyCode == '39') {
snakeVelocity = {
i: 1,
j: 0
};
}
}
document.onkeydown = checkKey;
setInterval(update, 1000 / 20);
drawSnake();
</script>
）如何调整代码以使其适用于彩色图像 - 所以图像有三个通道？
答案 0 :(得分：1)
最简单的方法是将图像分割成通道，然后在重新映射后，将它们合并在一起。
# make sure it's not gray
assert len(image.shape) == 3
# grab the image resolution
shape = image.shape[:2]
# random_state, gaussian and meshgrid
.
.
.
#calculate indices just as before
indices = np.reshape(x+dx, (-1, 1)), np.reshape(y+dy, (-1, 1))
# split the image into 3 channels
b, g, r = cv2.split(image)
# do the mapping on all of the channels separately
b = map_coordinates(b, indices, order=1).reshape(shape)
g = map_coordinates(g, indices, order=1).reshape(shape)
r = map_coordinates(r, indices, order=1).reshape(shape)
# return merged result
return cv2.merge((b,g,r))