How to test a video or a image folder in demo_eval.py

[huizeng]

How to test a video or a image folder in demo_eval.py

[HuiZeng]

For this purpose, you need to write some lines of code to read and process a set of images.
You can check image_adaptive_lut_evaluation.py for reference.

[xuhui1994]

非常感谢你的帮助，这份代码有些乱码，你能用txt文件保存再发给我吗？

…

------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "HuiZeng/Image-Adaptive-3DLUT" <[email protected]&gt;; 发送时间:&nbsp;2020年11月17日(星期二) 下午5:25 收件人:&nbsp;"HuiZeng/Image-Adaptive-3DLUT"<[email protected]&gt;; 抄送:&nbsp;"威爵"<[email protected]&gt;;"Mention"<[email protected]&gt;; 主题:&nbsp;Re: [HuiZeng/Image-Adaptive-3DLUT] How to test a video or a image folder in demo_eval.py (#23) @xuhui1994 可以修改demo_eval.py文件如下 **

---------- test

---------- read image and transform to tensor if opt.input_color_space == 'sRGB': img = Image.open(opt.image_path) img = TF.to_tensor(img).type(Tensor) elif opt.input_color_space == 'XYZ': img = cv2.imread(opt.image_path, -1) img = np.array(img) img = TF_x.to_tensor(img).type(Tensor) capture = cv2.VideoCapture(-1) capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640) capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) out_img = np.zeros(shape=(480, 640, 3), dtype=np.uint8) while (capture.isOpened()): bt = time.time() ret, ori_img = capture.read() img = np.array(ori_img) img = TF_x.to_tensor(img).type(Tensor) img = img.unsqueeze(0) # print(img.shape) LUT = generate_LUT(img) # print(LUT.shape) # generate image result = trilinear_(LUT, img)[1] # print(result.shape) # save image ndarr = result.squeeze().mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy() # out_img[:, :320, :] = ori_img[:, :320, :] # out_img[:, 320:, :] = ndarr[:, 320:, :] # im = Image.fromarray(ndarr) print("[INFO]&gt;&gt;&gt; 用时:{}".format(time.time() - bt)) # cv2.imwrite('%s/result.jpg' % opt.output_dir, ndarr) # im.save('%s/result.jpg' % opt.output_dir, quality=95) cv2.imshow('frame', ndarr) if cv2.waitKey(1) == ord('q'): break capture.release() cv2.destroyAllWindows() ** — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.

[Gager-Git-life]

抱歉git上看到太混乱了就删了😅 xuhui1994 <[email protected]> 于2020年11月17日周二 下午5:39写道：

…

非常感谢你的帮助，这份代码有些乱码，你能用txt文件保存再发给我吗？ ------------------&nbsp;原始邮件&nbsp;------------------ 发件人: "HuiZeng/Image-Adaptive-3DLUT" ***@***.***&gt;; 发送时间:&nbsp;2020年11月17日(星期二) 下午5:25 收件人:&nbsp;"HuiZeng/Image-Adaptive-3DLUT"< ***@***.***&gt;; ***@***.******@***.***&gt;; 主题:&nbsp;Re: [HuiZeng/Image-Adaptive-3DLUT] How to test a video or a image folder in demo_eval.py (#23) @xuhui1994 可以修改demo_eval.py文件如下 ** ---------- test ---------- read image and transform to tensor if opt.input_color_space == 'sRGB': img = Image.open(opt.image_path) img = TF.to_tensor(img).type(Tensor) elif opt.input_color_space == 'XYZ': img = cv2.imread(opt.image_path, -1) img = np.array(img) img = TF_x.to_tensor(img).type(Tensor) capture = cv2.VideoCapture(-1) capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640) capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) out_img = np.zeros(shape=(480, 640, 3), dtype=np.uint8) while (capture.isOpened()): bt = time.time() ret, ori_img = capture.read() img = np.array(ori_img) img = TF_x.to_tensor(img).type(Tensor) img = img.unsqueeze(0) # print(img.shape) LUT = generate_LUT(img) # print(LUT.shape) # generate image result = trilinear_(LUT, img)[1] # print(result.shape) # save image ndarr = result.squeeze().mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy() # out_img[:, :320, :] = ori_img[:, :320, :] # out_img[:, 320:, :] = ndarr[:, 320:, :] # im = Image.fromarray(ndarr) print("[INFO]&gt;&gt;&gt; 用时:{}".format(time.time() - bt)) # cv2.imwrite('%s/result.jpg' % opt.output_dir, ndarr) # im.save('%s/result.jpg' % opt.output_dir, quality=95) cv2.imshow('frame', ndarr) if cv2.waitKey(1) == ord('q'): break capture.release() cv2.destroyAllWindows() ** — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#23 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALLUMMI665C5N43AYJOLKITSQJADJANCNFSM4S7CTQDA> .

[Serendipity-55428]

非常感谢你的帮助，这份代码有些乱码，你能用txt文件保存再发给我吗？
…

Could you forward this txt to me, please? My mailbox is [email protected] and I'm curious if you've successfully tested a video in in demo_eval.py. I noticed that the author took part in the competition of NAIC2019 4K HDR and won the championship , which used this method for a video.

[erbaymustafa]

You can use the code below...

***Just describe the folder that contains the images on line 75 and edit the split method according to your file path on line 106
***Create a folder named 'output'

import argparse
import torch
import os
import numpy as np
import cv2
from PIL import Image
import time
from models_x import *
import torchvision_x_functional as TF_x
import torchvision.transforms.functional as TF
import glob

parser = argparse.ArgumentParser()

parser.add_argument("--image_dir", type=str, default="demo_images", help="directory of image")
parser.add_argument("--image_name", type=str, default="frame254.tif", help="name of image")
parser.add_argument("--input_color_space", type=str, default="sRGB", help="input color space: sRGB or XYZ")
parser.add_argument("--model_dir", type=str, default="pretrained_models", help="directory of pretrained models")
parser.add_argument("--output_dir", type=str, default="demo_results", help="directory to save results")
opt = parser.parse_args()
opt.model_dir = opt.model_dir + '/' + opt.input_color_space
#opt.image_path = opt.image_dir + '/' + opt.input_color_space + '/' + opt.image_name

os.makedirs(opt.output_dir, exist_ok=True)

# use gpu when detect cuda
cuda = True if torch.cuda.is_available() else False
# Tensor type
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor

criterion_pixelwise = torch.nn.MSELoss()
LUT0 = Generator3DLUT_identity()
LUT1 = Generator3DLUT_zero()
LUT2 = Generator3DLUT_zero()
#LUT3 = Generator3DLUT_zero()
#LUT4 = Generator3DLUT_zero()
classifier = Classifier()
trilinear_ = TrilinearInterpolation() 

if cuda:
    LUT0 = LUT0.cuda()
    LUT1 = LUT1.cuda()
    LUT2 = LUT2.cuda()
    #LUT3 = LUT3.cuda()
    #LUT4 = LUT4.cuda()
    classifier = classifier.cuda()
    criterion_pixelwise.cuda()

# Load pretrained models
LUTs = torch.load("%s/LUTs.pth" % opt.model_dir)
LUT0.load_state_dict(LUTs["0"])
LUT1.load_state_dict(LUTs["1"])
LUT2.load_state_dict(LUTs["2"])
#LUT3.load_state_dict(LUTs["3"])
#LUT4.load_state_dict(LUTs["4"])
LUT0.eval()
LUT1.eval()
LUT2.eval()
#LUT3.eval()
#LUT4.eval()
classifier.load_state_dict(torch.load("%s/classifier.pth" % opt.model_dir))
classifier.eval()


def generate_LUT(img):

    pred = classifier(img).squeeze()
    
    LUT = pred[0] * LUT0.LUT + pred[1] * LUT1.LUT + pred[2] * LUT2.LUT #+ pred[3] * LUT3.LUT + pred[4] * LUT4.LUT

    return LUT


#Describe the folder that contains the images
path = glob.glob('***Images Folder Path***/*.jpg')
# ----------
#  test
# ----------
# read image and transform to tensor


for file in path:

    start = time.time()
    if opt.input_color_space == 'sRGB':
        img = cv2.imread(file)
        img = TF.to_tensor(img).type(Tensor)
    elif opt.input_color_space == 'XYZ':
        img = cv2.imread(opt.image_path, -1)
        img = np.array(img)
        img = TF_x.to_tensor(img).type(Tensor)
    img = img.unsqueeze(0)

    LUT = generate_LUT(img)

    # generate image
    _, result = trilinear_(LUT, img)
    
    # save image
    ndarr = result.squeeze().mul_(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
    im = Image.fromarray(ndarr)
    im = np.array(im)
    
    finish = time.time()
    #you can change this split method for your file path 
    name_img = file.split('/')
    name_img = name_img[5]

    print(name_img)
    #Create a folder named output
    cv2.imwrite('output/'+ name_img , im)
    print('Image was enhanced')
    print('Time stamp: '+str(finish-start))