后续会更新:将 js 代码等打包成 chrome 扩展程序,这样就可以让浏览器自动识别,完全傻瓜式使用啦~!...只是当时我写代码的时候,没有找到纯 js 可以用的代码和库,不能打包成 chrome 扩展,用起来还是不太方便的。所以在验证了思路的可行性后,我就大致写下来,给他人以方便吧。...[j].length-1;k++){ if(fromArray[j][k]==1&&fromArray[j-1][k]+fromArray[j+1][k]+fromArray[j...训练完成后,我将数据库数据导出,保存成了一个大的数组,直接用 js 就可以读了。识别时遍历所有的数据,按像素点逐一比较。由于尺寸做了归一化,所以直接数有多少像素匹配即可。...我只找到了最开始写的 PHP 代码,先放一下吧,有点懒得再写 js 了…: function check($str) { $str = str_split($str,1); $length
of.js的实现很简单: ? import { isScheduler } from '../util/isScheduler'; import { fromArray } from '..../fromArray'; import { scheduleArray } from '.....(args); } } //# sourceMappingURL=of.js.map 没有scheduler,走fromArray这条路: ?...fromArray.js的实现: import { Observable } from '../Observable'; import { subscribeToArray } from '.....input)); } else { return scheduleArray(input, scheduler); } } //# sourceMappingURL=fromArray.js.map
我这个例子里,只执行下面这行语句,其他 IF 分支都没有进去: return fromArray(observables, scheduler).lift(new CombineLatestOperator...(resultSelector)) 首先执行 fromArray:输入是 Array,包含两个元素: ?...fromArray: 返回一个新的 Observable,输入是subscribeToArray(input)....scan.js 的内部实现: ? ? accumulator 就是应用程序自定义的函数: ? 其中 acc 就是 scan.js 里的 seed,而 curr 即是当前值。
(tif32).save('.testlena32_Image.tif') #-- 32bit Image.fromarray(tif16).save('.testlena16_Image.tif')...#-- 16bit Image.fromarray(tif8).save('.testlena8_Image.tif') #-- 8bit Image.fromarray(flt).save('....test\randmat_Image.tif') #-- 32bit(flt.min~flt.max) im = Image.fromarray(flt.astype(np.float32))...(z16Norm) im.save('.test\randmat16_norm_Image.tif') #-- 32bit(0~1) im = Image.fromarray(z16Norm.astype...(np.float32)) im.save('.test\randmat16_norm_to32_Image.tif') #-- 32bit(灰度范围值同上) im = Image.fromarray
/bootstrap/js/bootstrap.js"> var camera, scene, renderer; var geometry,...element.src = side.url; var object = new THREE.CSS3DObject(element); object.position.fromArray...(side.position); object.rotation.fromArray(side.rotation); scene.add(object);
blue, sigma=1.5) gaussian = np.dstack((redGaussian, greenGaussian, blueGaussian)) gaussianImg = Image.fromarray...ndimage.uniform_filter(blue, size=15) box = np.dstack((redBox, greenBox, blueBox)) boxImg = Image.fromarray...15) percentile = np.dstack((redPercentile, greenPercentile, bluePercentile)) percentileImg = Image.fromarray...1, 1]])) correlate = np.dstack((redCorrelate, greenCorrelate, blueCorrelate)) correlateImg = Image.fromarray...whiteTophat = np.dstack((redWhiteTophat, greenWhiteTophat, blueWhiteTophat)) whiteTophatImg = Image.fromarray
# axis=0 is vertical, axis=1 is horizontal verticalData = np.flip(data, axis=0) verticalImg = Image.fromarray...axis=0 is vertical, axis=1 is horizontal horizontalData = np.flip(data, axis=1) horizontalImg = Image.fromarray...# axis=0 is vertical, axis=1 is horizontal verticalData = np.flip(data, axis=0) verticalImg = Image.fromarray...return flip_180_with_axis(flip_left_90(arr)) left90Data = flip_left_90(data) left90Img = Image.fromarray...right90DataFromAxisLeft90 = flip_right_90_with_axis_left_90(data) right90ImgFromAxisLeft90 = Image.fromarray
今天郭先生来说一说three.js的三维矩阵,这块知识需要结合线性代数的一些知识,毕业时间有点长,线性代数的知识大部分都还给了老师。于是一起简单的复习了一下。 所有的计算都是使用列优先顺序进行的。...然而,由于实际的排序在数学上没有什么不同, 而且大多数人习惯于以行优先顺序考虑矩阵,所以three.js文档以行为主的顺序显示矩阵。...this.setFromMatrix4( matrix4 ).getInverse( this ).transpose(); 上面试three.js的源码,从源码可以看出这是setFromMatrix4...15. fromArray( array: number[], offset?: number ): Matrix3 使用基于列优先格式column-major的数组来设置该矩阵。...这是fromArray的逆运算。
这个函数返回的仍然是Observable,所以可以继续使用Pipe ob1:=Pipe(FromArray(...),Filter(...),...) Pipe(ob1,Map(...)...最终我们可以如此调用: rx.FromArray(...).Filter(...).Subscribe(...) 那么如何实现呢?...func FromArray(array []interface{}) *Observable { return &Observable{p.FromArray(array)} } 当我们调用rx.FromArray...(...)的时候,会返回一个rx.Observable 的对象指针,这个对象里面的source属性就是pipe包里面的FromArray函数调用后的Observable 当我们继续调用操作符Filter...的时候,rx.FromArray(...).Filter(...)
the_starry_night.jpg')) # left 90 rot90LeftWithOne = np.rot90(data, 1) rot90LeftWithOneImg = Image.fromarray...rot90RightWithThree = np.rot90(data, 3) rot90RightWithThreeImg = Image.fromarray(rot90RightWithThree...rot90RightWithMinusOne = np.rot90(data, -1) rot90RightWithMinusOneImg = Image.fromarray(rot90RightWithMinusOne...') 或者将轴转置旋转 rot90RightWithAOnexes = np.rot90(data, 1, axes=(1,0)) rot90RightWithAOnexesImg = Image.fromarray...rot180WithLeftTwice = np.rot90(data, 2) rot180WithLeftTwiceImg = Image.fromarray(rot180WithLeftTwice
np.uint16) + np.min(data, axis=-1).astype(np.uint16)) / 2).astype(np.uint8) lightnessGreyImg = Image.fromarray...3).astype(np.uint8) # averageGrey = np.mean(data, axis=2).astype(np.uint8) averageGreyImg = Image.fromarray...luminosityGrey = np.dot(data[...,:3], [0.2989, 0.5870, 0.1140]).astype(np.uint8) luminosityGreyImg = Image.fromarray...(luminosityGrey) luminosityGreyImg.save('luminosity_grey.png') 还有一种写法就是用PIL库 lGreyImg = Image.fromarray
; debugger; newFunction(subscriber); 输出: 回到 fromArray.js 的 subscribeToArray,也就不难理解其逻辑了
np.array(Image.open('the_starry_night.jpg')) 左旋转30度,且重新调整图片大小 left30 = ndimage.rotate(data, 30) Image.fromarray...(left30).save('left30.png') 右旋转30度,且重新调整图片大小 right30 = ndimage.rotate(data, -30) Image.fromarray(right30...,保持图片大小不变 注意我们给reshape参数传递了False,即不调整图片大小 left135 = ndimage.rotate(data, 135, reshape=False) Image.fromarray...left135).save('left135.png') 右旋转135度,保持图片大小不变 right135 = ndimage.rotate(data, -135, reshape=False) Image.fromarray
img_pad) img3 = img_pad[::-1, :] img_cat = np.concatenate([img_pad, img1, img2, img3], axis=1) Image.fromarray...img_pad) img3 = img_pad[:, ::-1] img_cat = np.concatenate([img_pad, img1, img2, img3], axis=1) Image.fromarray...实验: img1 = np.transpose(img_pad, (1, 0)) img_cat = np.concatenate([img_pad, img1], axis=1) Image.fromarray...2) img3 = np.rot90(img_pad, 3) img_cat = np.concatenate([img_pad, img1, img2, img3], axis=1) Image.fromarray...2) img3 = np.rot90(img_pad, 3) img_cat = np.concatenate([img_pad, img1, img2, img3], axis=1) Image.fromarray
0.299,0.587,0.114],[0.299,0.587,0.114],[0.299,0.587,0.114]]).transpose() im = np.dot(im,trans) return Image.fromarray...im1 = np.sqrt(im*[1.0,0.0,0.0])*params im2 = im*[0.0,1.0,1.0] im = im1+im2 return Image.fromarray...() # clip 超过255的颜色置为255 im = np.dot(im,trans).clip(max=255) return Image.fromarray...reverse(imagename): im = 255 - np.asarray(Image.open(imagename).convert('RGB')) return Image.fromarray
hight=douyin.size array_r=np.array(douyin) array_r[:, :, 1] = 0 array_r[:, :, 2] = 0 douyin_r=Image.fromarray...np.array(douyin) array_b[:, :, 0] = 0 array_b[:, :, 1] = 0 array_gb=array_g+array_b douyin_gb=Image.fromarray...douyin_gb,(10,10)) array_gb_new=np.array(temp_pic) res_array=array_r+array_gb_new douyin_res=Image.fromarray
tmp_img0 = np.zeros(img.shape,dtype='uint8') tmp_img0[:,:,0] = img[:,:,0] # 提取第一个通道 tmp_im0=Image.fromarray...同理分别分离出第二颜色通道: tmp_img1 = np.zeros(img.shape,dtype='uint8') tmp_img1[:,:,1] = img[:,:,1] tmp_im1=Image.fromarray...第三颜色通道: tmp_img2 = np.zeros(img.shape,dtype='uint8') tmp_img2[:,:,2] = img[:,:,2] tmp_im2=Image.fromarray...3 裁剪图像 我们直接切片 img 数组, img_slice = img[500:1000,300:700,:] # 切片图像 img_slice2 = Image.fromarray(img_slice
reshape((3, 32, 32)) r, g, b = sample[0], sample[1], sample[2] r0, g0, b0 = Image.fromarray...(r), Image.fromarray(g), Image.fromarray(b) sample_rgb = Image.merge('RGB', (r0, g0, b0))
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