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test.py
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test.py
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import argparse
import os
import cv2
import numpy as np
from ctypes import *
import ctypes
import cv2
import numpy as np
import sys
import os
import cv2
import sys
import importlib
import imageio
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
# vod =cdll.LoadLibrary("/home/yanjianfeng/.local/libs/libvod.so")
# vls=cdll.LoadLibrary("../build/src/libmobile_vls.so")
#
#
#
# def sharpening(instance,image,size=512):
# img0=image.astype(np.uint8)
# input = cast(image.ctypes.data,POINTER(c_uint8))
# nullptr=cast(None,POINTER(c_uint8))
#
# output = np.zeros_like(image,np.uint8)
# output = cast(output.ctypes.data,POINTER(c_uint8))
#
# state=vls.vls_process_frame_luminance(instance,
# input,nullptr,0,image.shape[1],image.shape[0],
# image.shape[1],image.shape[1],output)
# print(state)
# output1=np.ctypeslib.as_array(
# (ctypes.c_uint8*image.size).from_address(ctypes.addressof(output.contents)))
# output1=output1.reshape(img0.shape).astype(np.uint8)
#
#
# # output2 = np.zeros_like(image,np.uint8)
# # output2 = cast(output2.ctypes.data,POINTER(c_uint8))
#
# # vls.vls_process_frame_luminance(instance,
# # input,nullptr,image.shape[1],image.shape[0],
# # image.shape[1],image.shape[1],output2,False)
# # output2=np.ctypeslib.as_array(
# # (ctypes.c_uint8*image.size).from_address(ctypes.addressof(output2.contents)))
# # output2=output2.reshape(img0.shape).astype(np.uint8)
# return output1
import torch
import torch.nn.functional as F
import model
loadckpt = "/home/yanjianfeng/data/srnet_model_grad_Scharr_x_y_gen_1.5/snet_model_000010.ckpt"
snet= model.SRNet(1).cuda()
# snet = snet.cuda()
state_dict = torch.load(loadckpt,map_location=torch.device('cuda'))
# print(state_dict['model'])
snet.load_state_dict(state_dict['model'], strict=False)
# snet = snet.cpu()
import matplotlib.pyplot as plt
def sharpen1(y,max_scale=5):
y = y.astype(np.float32)/255.0
# cv2.waitKey()
y_tensor = torch.Tensor(y[np.newaxis,np.newaxis]).cuda()
#
b = snet(y_tensor).cuda()
# b = cv2.ximgproc.guidedFilter(y,y,radius=1,eps=0.01)
patch = torch.nn.functional.unfold(y_tensor, kernel_size=3, dilation=1, padding=1, stride=1)
patch = patch.reshape([1,9,y_tensor.shape[2],y_tensor.shape[3]])
mean_patch = patch.mean(dim=1,keepdim=True)
vp = torch.mean((patch- mean_patch)**2,1,keepdim=True)
# print("std:",vp[vp>0].mean())
vp = torch.clamp(vp*500,0,2.0)
# # mean = y_tensor.mean(dim=1,keepdim)
# # patch = torch.nn.functional.unfold(y_tensor, kernel_size=3, dilation=1, padding=1, stride=1)
# # patch = patch.reshape([1, 9, y_tensor.shape[2], y_tensor.shape[3]])
# # mean_patch = patch.mean(dim=1, keepdim=True)
# # vp = torch.mean((patch - mean_patch) ** 2, 1, keepdim=True)
# detail = y_tensor-b
# mean_detail = detail.mean(dim=1,keepdim=True)
# ve = torch.mean((detail- mean_detail)**2,1,keepdim=True)
# mask = ve<1e-4
# ve[mask]=1e-4
# k=1.03
# scale1=torch.clamp(vp*100,0,5.0)
# scale1 = scale1.squeeze().squeeze().detach().cpu().numpy()
# e = (y_tensor-b).squeeze().squeeze().detach().cpu().numpy()
# print(e.min(),e.max(),scale1.min(),scale1.max())
# cv2.imshow("scale", scale1)
# cv2.waitKey()
# plt.imshow(scale1,'rainbow'),plt.show()
# dpx = cv2.Sobel(y,cv2.CV_32F,1,0,ksize=3)
# dpy = cv2.Sobel(y,cv2.CV_32F,0,1,ksize=3)
# # cv2.imshow("orid",15*d)
# dex = cv2.Sobel(e,cv2.CV_32F,1,0,ksize=3)
# dey = cv2.Sobel(e,cv2.CV_32F,0,1,ksize=3)
# pxex = dpx*dex
# mask = np.abs(pxex)<1e-4
# pxex[mask]=1.0
# scale = -dpx*dpx/pxex
#
# scale = np.where(pxex<0,scale,max_scale)
# scale[np.bitwise_and(mask,np.abs(dpx)<1e-3)]=0
#
# scale = np.where(scale<scale1,scale,scale1)
# scale1[np.bitwise_and(mask,np.abs(dpx)<1e-3)]=0
#
# scale = np.clip(scale,0,max_scale)
# cv2.imshow("scale",scale/max_scale)
# b = F.avg_pool2d(b,3,1,1)
sharp = (y_tensor*(1+b))
# maxs = F.max_pool2d(y_tensor,3,1,1)
# mins = -F.max_pool2d(-y_tensor,3,1,1)
# # maxs = maxs.reshape(y.shape).numpy()
# # mins = mins.reshape(y.shape).numpy()
# alpha=0.25
# sharp[sharp<mins] = (mins+alpha*(sharp-mins))[sharp<mins]
# sharp[sharp>maxs] = (maxs+alpha*(sharp-maxs))[sharp>maxs]
# mask = (maxs-mins)<10.0/255
# sharp[mask]=y[mask]
sharp = sharp.squeeze().squeeze().detach().cpu().numpy()
sharp = np.clip(sharp*255.0,0,255.0).astype(np.uint8)
return sharp
import time
def enhance_video(instance,video_path,output_dir):
# input_dir="/home/yanjianfeng/data/like_data/"
# output_dir=os.path.join(input_dir,'enhanced')
if os.path.exists(output_dir)==False:
os.makedirs(output_dir)
# video_path='/home/yanjianfeng/data/test_video/clip110_400x720_24_728.mp4'
cap = cv2.VideoCapture(video_path)
# output_dir="/home/yanjianfeng/data/outut_video"
if os.path.exists(output_dir)==False:
os.makedirs(output_dir)
if cap.isOpened()==False:
exit
# fourcc = cv2.VideoWriter_fourcc(*'DIVX') # 保存视频的编码
fourcc=int(cap.get(cv2.CAP_PROP_FOURCC))
fps = cap.get(cv2.CAP_PROP_PVAPI_MULTICASTIP)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = None
# print(out)
idx=0
times=[]
while(1):
ret, img = cap.read()
print(cap.get(cv2.CAP_PROP_FORMAT))
# print("{",img.shape[1],",",img.shape[0],"},")
# break
if ret==False:
break
print(idx)
idx+=1
if idx==1:
print(img.shape)
# if (img.shape[0]%4 !=0)|(img.shape[1]%4 !=0):
# break
# vls.vls_set_new_video_info(instance,img.shape[1],img.shape[0],0,255)
if idx>200:
break
img=img.astype(np.uint8)
yuv=cv2.cvtColor(img,cv2.COLOR_BGR2YUV)
lumi = yuv[...,0]+0.0
y=np.ascontiguousarray(yuv[...,0],dtype=np.uint8)
# cv2.imshow("ori",yuv[...,0])
# print(y.flags['C_CONTIGUOUS'])
# print(y.shape)
# output=sharpening(instance,y)
# output=o0.astype(np.uint8)
# print(output.astype(np.float32).mean())
# print(y.shape)
# yuv[...,0]=o0
# rgb1=cv2.cvtColor(yuv,cv2.COLOR_YUV2BGR)
o1 =sharpen1(y,20.0)
yuv[...,0]=o1
rgb2= cv2.cvtColor(yuv,cv2.COLOR_YUV2BGR)
# yuv[...,0]=o1
# rgb2=cv2.cvtColor(yuv,cv2.COLOR_YUV2RGB)
# img[:,:img.shape[1]//2:,:]=rgb1[:,img.shape[1]//2:,:]
# rgb2[:,rgb2.shape[1]//2:,...]=img[:,rgb2.shape[1]//2:,...]
# rgb2[:,rgb2.shape[1]//2,...]=0
# output=np.concatenate([lumi,o0*20,o1*20],1)
output = np.concatenate([img,rgb2],1)
# output=o0
# print(output.shape)
# output=rgb1
if out is None:
video_name=os.path.splitext(os.path.basename(video_path))[0]
out = cv2.VideoWriter(os.path.join(output_dir,video_name+'.mp4'), cv2.VideoWriter_fourcc(*'mp4v'), 17, (output.shape[1],output.shape[0]))
out.write(output.astype(np.uint8))
video_name=os.path.splitext(os.path.basename(video_path))[0]
out_dir = os.path.join(output_dir,video_name)
if os.path.exists(out_dir) == False:
os.makedirs(out_dir)
cv2.imwrite(os.path.join(out_dir,"enhance_%d.jpg"%idx),output)
# cv2.imwrite(os.path.join(out_dir,"thumb%03d.jpg"%idx),img)
# break
# cv2.imshow("enhanced",output.astype(np.uint8))
# cv2.waitKey()
# cv2.imwrite("enhance_%d.jpg"%idx,output)
# break
# cap.release()
# if out is not None:
# out.release()
# input = cast(np.zeros([6],dtype=np.float32).ctypes.data,POINTER(c_float))
# vls.vls_get_report(instance,input,6)
# output=np.ctypeslib.as_array(
# (ctypes.c_float*6).from_address(ctypes.addressof(input.contents)))
# output[-2]=output[-2]/output[1]
# print(output)
if __name__ == "__main__":
output_dir="/home/yanjianfeng/data/douyin_out_cnn_5_std_2/"
input_dir="/home/yanjianfeng/data/douyin/"
instance = c_uint64(0)
# status=vls.vls_create_instance(byref(instance))
#
# status = vls.vls_init(instance,0)
# vls.vls_set_enhance_scale(instance,10)
# vls.vls_set_noise_scale(instance,3)
# print(status)
for video in sorted(os.listdir(input_dir)):
# if (video.find("clip15_")==-1 ) :
# continue
# if os.path.exists(os.path.join(output_dir,video)):
# continue
video_path=os.path.join(input_dir,video)
print(video_path,'enhancing...')
enhance_video(instance,video_path,output_dir)
# # enhance_video(instance,"/home/haibao637/data/bigolive_dumping/uid_1537369101_date_1-29-21-5-7.264",output_dir)
# # img = cv2.imread("/home/haibao637/data/like_real_test_out_detail/256LNc/thumb_1.bmp")
# # yuv = cv2.cvtColor(img,cv2.COLOR_BGR2YUV)
# # vls.vls_set_new_video_info(instance,img.shape[1],img.shape[0],0,255)
# # o = sharpening(instance,yuv[...,0])
# # cv2.imwrite("o.bmp",o)
# # print(o.astype(np.float32).mean())
# # vls.vls_release_resource(instance)
# # vls.vls_destroy_instance(byref(instance))
# enhance_video(0,"/home/haibao637/data/clip36_400x720_18_1667.mp4","output/")
# img = cv2.imread("//home/haibao637/data/like_real_test_out_detail/256LNc/thumb_302.bmp")
# img = img.astype(np.float32)/255.0
# yuv = cv2.cvtColor(img,cv2.COLOR_BGR2YUV)
# y = np.ascontiguousarray(yuv[...,0])
# sharp = sharpen1(y)
# sharp = np.clip(sharp,0,1.0)
# yuv[...,0]=sharp
# rgb = cv2.cvtColor(yuv,cv2.COLOR_YUV2BGR)
# rgb = np.clip(rgb,0,1.0)
# cv2.imshow("ori",img)
# cv2.imshow("rgb",rgb)
# delta = sharp - y
# cv2.imshow("delta",(delta*10.0+1.0)/2.0)
# cv2.waitKey()