尽管NeRF可以渲染出高质量的新视角图像,但前提是有高质量的图像作为输入以及精确的相机参数。而一旦输入的图像有明显缺陷时,原始的NeRF就难以实现高质量的渲染。过去已经有一部分工作在围绕NeRF与有缺陷的输入图像来展开,NeRF-W针对场景亮度的变化和移动的物体;Mip-NeRF针对不同分辨率的输入图像;SCNeRF主要针对输入图像的畸变。
——王尔德 安卓实现一个毛玻璃效果可以使用Blurry https://github.com/wasabeef/Blurry 代码: // 覆盖,父级必须是 ViewGroup Blurry.with( context).radius(25).sampling(2).onto(rootView) // 添加 // from View Blurry.with(context).capture(view). into(imageView) // from Bitmap Blurry.with(context).from(bitmap).into(imageView) Blur Options 模糊选项 Radius 半径 Down Sampling 下采样 Color Filter 彩色滤光片 Asynchronous Support 异步支持 Animation (Overlay Only) 动画(仅限叠加) Blurry.with 0)) .async() .animate(500) .onto(rootView); Get a bitmap directly 直接获取位图 // Sync val bitmap = Blurry.with
import cv2 import os import numpy as np from mtcnn import MTCNN # Function to check if an image is blurry def is_blurry(image_path, threshold=100): image = cv2.imread(image_path) gray = cv2.cvtColor(image, (image_path): print(f"Deleting blurry image: {filename}") os.remove(image_path) deleted_blurry += 1 continue 在上面的脚本中,is_blurry 函数默认使用的阈值是 100。 个人测试,如果是手机拍摄的照片,阈值设置为20,会比较好; def is_blurry(image_path, threshold=20):
image = np.dstack([gray] * 3) color = (0, 0, 255) if blurry else (0, 255, 0) text = "Blurry ({:.4f}) = "Blurry ({:.4f})" if blurry else "Not Blurry ({:.4f})" text = text.format(mean) cv2.putText : 9, Result: Blurry (7.8893) [INFO] Kernel: 11, Result: Blurry (0.6506) [INFO] Kernel: 13, Result: Blurry Blurry (9.1489) [INFO] Kernel: 15, Result: Blurry (2.3377) [INFO] Kernel: 17, Result: Blurry (-2.6372 ) if blurry else (0, 255, 0) text = "Blurry ({:.4f})" if blurry else "Not Blurry ({:.4f})" text =
photo of a highly detailed,(full body:1.2), smile,cute face ,,(studio background),(clean background),(blurry cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry photo of a highly detailed,(full body:1.2), smile,cute face ,,(studio background),(clean background),(blurry cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry
• 解决“大型猫科动物(greast cats)”(狮子或豹子等)被错认家猫(宠物)的问题 • 提高系统在模糊(Blurry)图像上的表现 • … 你可以并行并且有效的评估这些想法。 表格中Image3的Great cat和Blurry列都被勾选了:可以将一个样本与多个类别相关联, 这就是为什么最后的百分比加起来不足100%的原因。 虽然我已经将这个过程首先描述为类别分类(Dog, Great cat, Blurry), 然后查看样例对他们进行分类。实际中,当你查看样例时,可能会受到启发,然后提出一些新的错误类别。 而致力于Great cat和Blurry对你的帮助更大。因此,你可能会挑选后者之一来进行处理。 如果你的团队有足够多的人可以同时展开多个方向,你让一部分人解决Great cat问题,另一部分人解决Blurry问题。 错误分析并不会得出一个明确的数学公式来告诉你最应该先处理哪个问题。
age spot, glans,extra fingers,fewer fingers,strange fingers,bad hand,signature, watermark, username, blurry disgusting, poorly drawn hands, missing limb, floating limbs, disconnected limbs, malformed hands, blurry age spot, glans,extra fingers,fewer fingers,strange fingers,bad hand,signature, watermark, username, blurry
查看当前负面提示词 提示词> neg 当前负面提示词: ugly, deformed, distorted, blurry, low quality, bad anatomy, extra legs 修改负面提示词 提示词> neg:blurry, ugly, low quality [负面提示词已更新为: blurry, ugly, low quality] 4. , elegant dress, 8k # 切换到风景 提示词> neg:blurry, low quality 提示词> beautiful mountain landscape, sunset, 设置人物专用负面提示词 提示词> neg:bad hands, bad fingers, ugly, deformed, blurry, low quality 3. 设置风景专用负面提示词 提示词> neg:blurry, low quality, overexposed 2.
• 解决“大型猫科动物(greast cats)”(狮子或豹子等)被错认家猫(宠物)的问题 • 提高系统在模糊(Blurry)图像上的表现 • … 你可以并行并且有效的评估这些想法。 用有小开发集里的4个错误分类样本来说明这个过程,你的表格大概将会是下面的样子: 表格中Image3的Great cat和Blurry列都被勾选了:可以将一个样本与多个类别相关联, 这就是为什么最后的百分比加起来不足 虽然我已经将这个过程首先描述为类别分类(Dog, Great cat, Blurry), 然后查看样例对他们进行分类。实际中,当你查看样例时,可能会受到启发,然后提出一些新的错误类别。 而致力于Great cat和Blurry对你的帮助更大。因此,你可能会挑选后者之一来进行处理。 如果你的团队有足够多的人可以同时展开多个方向,你让一部分人解决Great cat问题,另一部分人解决Blurry问题。 错误分析并不会得出一个明确的数学公式来告诉你最应该先处理哪个问题。
(image) blurry_frames = np.array(blurry_frames) 导入模型库 from keras.layers import Dense, Inputfrom keras.layers x = clean_frames;y = blurry_frames; from sklearn.model_selection import train_test_splitx_train, x_test wspace=0.2)ax = fig.add_subplot(1, 2, 1)ax.imshow(clean_frames[r])ax = fig.add_subplot(1, 2, 2)ax.imshow(blurry_frames , clean_frames, validation_data=(blurry_frames, clean_frames), Predicted Value") for i in range(3): r = random.randint(0, len(clean_frames)-1) x, y = blurry_frames
ultra-detailed,highres,extremely detailed,beautiful detailed girl,realistic,full frontal,light contrast,blurry hands,text,error,missing fingers,extra digit,fewer digits,worstquality,jpegartifacts,signature,username,blurry ultra-detailed,highres,extremely detailed,beautiful detailed girl,realistic,full frontal,light contrast,blurry hands,text,error,missing fingers,extra digit,fewer digits,worstquality,jpegartifacts,signature,username,blurry
image).astype('float32') / 255 clean_frames.append(image) clean_frames = np.array(clean_frames) blurry_frames (image) blurry_frames = np.array(blurry_frames) 导入模型库 from keras.layers import Dense, Input from keras.layers x = clean_frames; y = blurry_frames; from sklearn.model_selection import train_test_split x_train, x_test 0.2) ax = fig.add_subplot(1, 2, 1) ax.imshow(clean_frames[r]) ax = fig.add_subplot(1, 2, 2) ax.imshow(blurry_frames , clean_frames, validation_data=(blurry_frames, clean_frames
cropped, worst quality, low quality,normal quality, jpeg artifacts, signature, watermark, username, blurry cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry nsexual, hermaphrodite,long neck,mutated hands,poorly drawn hands,poorly drawn face,mutation,deformed,blurry ((missing legs))), (((extra arms))),(((extra legs))),pubic hair, plump,bad legs,error,legs,username,blurry Shanghai负面关键词:(normal quality), (low quality), (worst quality), paintings, sketches,fog,signature,soft, blurry
long neck,long body,extra fingers,fewer fingers,,(multi nipples),bad hands,signature,username,bad feet,blurry long neck,long body,extra fingers,fewer fingers,,(multi nipples),bad hands,signature,username,bad feet,blurry long neck,long body,extra fingers,fewer fingers,,(multi nipples),bad hands,signature,username,bad feet,blurry long neck,long body,extra fingers,fewer fingers,,(multi nipples),bad hands,signature,username,bad feet,blurry long neck,long body,extra fingers,fewer fingers,,(multi nipples),bad hands,signature,username,bad feet,blurry
mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry 缺失的手臂、缺失的腿、额外的手臂、多余的腿、融合的手指、太多的手指、长脖子 出图测试: Negative prompt常用到的单词2 bad anatomy, bad proportions, blurry frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, ugly, blurry
duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.5), blurry
由于我们自己实现的Java版本的高斯模糊算法的效率太低,因此最后介绍比较有名的高斯模糊的开源项目:Blurry以及BlurKit-Android。 Demo4:Blurry的基本使用。 卷积 本文只讨论图像,而图像可以表示为二维矩阵,其中每个元素为ARGB像素值,因此这里讨论二维矩阵的卷积操作。 开源项目 关于Android图像模糊的开源项目有很多,比如Blurry是专门针对Bitmap或View做模糊,可以设置模糊的基底色,而且还能对模糊操作异步化;BlurKit-Android也能对Bitmap BlurKit-Android支持的最低版本是Android 4.1(API 16),因此如果应用需要支持的最低版本是4.0,则不能使用该库,Blurry支持的最低版本是3.0。 Blurry 配置方法:在build.gradle中添加compile 'jp.wasabeef:blurry:2.1.1'。 使用方法如下: ? 总的来说,这两个库都使用起来非常方便。
Laplacian # method image = cv2.imread(imagePath) fm = variance_of_laplacian(image) text = "Not Blurry # if the focus measure is less than the supplied threshold, # then the image should be considered "blurry " if fm < 100: text = "<em>Blurry</em>" # show the image file_name = os.path.basename(imagePath) cv2.imwrite
masterpiece, ultra detailed:1.2), cinematic angle, light particles, sparkle, (wide shot, depth of field:1.2), blurry mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry
nontraditional miko, shiny skin, long sleeves, smile, thick lips, game cg, east asian architecture, (blurry 0.02),nontraditional miko,shiny skin,long sleeves,smile,thick lips,game cg,east asian architecture,(blurry nontraditional miko, shiny skin, long sleeves, smile, thick lips, game cg, east asian architecture, (blurry