diff --git a/make_datasets/csv/stars_name.csv b/make_datasets/csv/stars_name.csv new file mode 100644 index 0000000000000000000000000000000000000000..4c0015bff73fc9359657c043b65518f445ae068c --- /dev/null +++ b/make_datasets/csv/stars_name.csv @@ -0,0 +1 @@ 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+071815_117-1pon-1080p.mp4,00:14:50,00:15:10,00:18:05,00:14:50,00:25:55,00:26:25,00:32:45,00:33:40,00:43:15,00:45:05,00:45:45,00:48:40,00:48:50,00:55:45,10:00:20,01:00:35,01:01:00,01:01:10 \ No newline at end of file diff --git a/make_datasets/cut_video.py b/make_datasets/cut_video.py new file mode 100644 index 0000000000000000000000000000000000000000..7b4d317124ee0bda4b4fd2447ac6918643c0d7a1 --- /dev/null +++ b/make_datasets/cut_video.py @@ -0,0 +1,33 @@ +import os +import numpy as np +import cv2 +import random +import csv + +import sys +sys.path.append("..") +from util import util,ffmpeg +from util import image_processing as impro + +files = util.Traversal('/media/hypo/Media/download') +videos = util.is_videos(files) + + +video_times = [] +useable_videos = [] +video_dict = {} +reader = csv.reader(open('./csv/video_used_time.csv')) +for line in reader: + useable_videos.append(line[0]) + video_times.append(line[1:]) + video_dict[line[0]]=line[1:] + +in_cnt = 0 +out_cnt = 502 +for video in videos: + if os.path.basename(video) in useable_videos: + # print(video) + for i in range(len(video_times[in_cnt])): + ffmpeg.cut_video(video, video_dict[os.path.basename(video)][i], '00:00:05', './video/'+'%04d'%out_cnt+'.mp4') + out_cnt +=1 + in_cnt += 1 diff --git a/make_datasets/draw_mask.py b/make_datasets/draw_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..69201ab804dd6ac88d4456e7b7e114c7ca0c5b7a --- /dev/null +++ b/make_datasets/draw_mask.py @@ -0,0 +1,97 @@ +import cv2 +import numpy as np +import datetime +import os +import random + +def resize(img,size): + h, w = img.shape[:2] + if w >= h: + res = cv2.resize(img,(int(size*w/h), size)) + else: + res = cv2.resize(img,(size, int(size*h/w))) + + return res + +# mouse callback function +drawing = False # true if mouse is pressed +ix,iy = -1,-1 +brushsize = 20 +def draw_circle(event,x,y,flags,param): + global ix,iy,drawing,brushsize + + if event == cv2.EVENT_LBUTTONDOWN: + drawing = True + ix,iy = x,y + + elif event == cv2.EVENT_MOUSEMOVE: + if drawing == True: + cv2.circle(img,(x,y),brushsize,(0,255,0),-1) + + elif event == cv2.EVENT_LBUTTONUP: + drawing = False + cv2.circle(img,(x,y),brushsize,(0,255,0),-1) + +def makemask(img): + # starttime = datetime.datetime.now() + mask = np.zeros(img.shape, np.uint8) + for row in range(img.shape[0]): + for col in range(img.shape[1]): + # if (img[row,col,:] == [0,255,0]).all(): #too slow + if img[row,col,0] == 0: + if img[row,col,1] == 255: + if img[row,col,2] == 0: + mask[row,col,:] = [255,255,255] + # endtime = datetime.datetime.now() + # print('Cost time:',(endtime-starttime)) + return mask + +files = os.listdir('./origin_image') +files_new =files.copy() +print('find image:',len(files)) +masks = os.listdir('./mask') +print('mask:',len(masks)) +for i in range(len(masks)): + masks[i]=masks[i].replace('.png','.jpg') +for file in files: + if file in masks: + files_new.remove(file) +files = files_new +# files = list(set(files)) #Distinct +print('remain:',len(files)) +random.shuffle (files) +# files.sort() +cnt = 0 + +for file in files: + cnt += 1 + img = cv2.imread('./origin_image/'+file) + img = resize(img,512) + cv2.namedWindow('image') + cv2.setMouseCallback('image',draw_circle) #MouseCallback + while(1): + + cv2.imshow('image',img) + k = cv2.waitKey(1) & 0xFF + if k == ord(' '): + img = resize(img,256) + mask = makemask(img) + cv2.imwrite('./mask/'+os.path.splitext(file)[0]+'.png',mask) + print('./mask/'+os.path.splitext(file)[0]+'.png') + # cv2.destroyAllWindows() + print('remain:',len(files)-cnt) + brushsize = 20 + break + elif k == ord('a'): + brushsize -= 5 + if brushsize<5: + brushsize = 5 + print('brushsize:',brushsize) + elif k == ord('d'): + brushsize += 5 + print('brushsize:',brushsize) + elif k == ord('w'): + print('remain:',len(files)-cnt) + break + +# cv2.destroyAllWindows() \ No newline at end of file diff --git a/make_datasets/use_addmosaic_model_make_dataset.py b/make_datasets/use_addmosaic_model_make_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..7c04dc0d669969bab847d928a917f3371a4b721a --- /dev/null +++ b/make_datasets/use_addmosaic_model_make_dataset.py @@ -0,0 +1,73 @@ +import sys +import os +import random +import datetime + +import numpy as np +import cv2 + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +from torch import optim + +from unet import UNet +from mosaic import random_mosaic +import image_processing as impro + + + +def runmodel(img,net): + img=impro.image2folat(img,3) + img=img.reshape(1,3,128,128) + img = torch.from_numpy(img) + img=img.cuda() + pred = net(img) + pred = (pred.cpu().detach().numpy()*255) + pred = pred.reshape(128,128).astype('uint8') + return pred + + + +dir_img = './origin_image/' +dir_mosaic = './mosaic/' +dir_mask = './mask/' +dir_dataset = './dataset/' +dir_checkpoint = 'checkpoints/' + +net = UNet(n_channels = 3, n_classes = 1) +net.load_state_dict(torch.load(dir_checkpoint+'mosaic_position.pth')) +net.cuda() +net.eval() +# cudnn.benchmark = True +files = os.listdir(dir_mosaic) + +for i,file in enumerate(files,1): + orgin_image = cv2.imread(dir_img+file) + mosaic_image = cv2.imread(dir_mosaic+file) + img = impro.resize(mosaic_image,128) + img1,img2 = impro.spiltimage(img) + mask1 =runmodel(img1,net) + mask2 =runmodel(img2,net) + mask = impro.mergeimage(mask1,mask2,img) + + # test_mask = mask.copy() + + mask = impro.mask_threshold(mask,blur=5,threshold=128) + if impro.mask_area(mask) > 1: + h,w = orgin_image.shape[:2] + mosaic_image = cv2.resize(mosaic_image,(w,h)) + # test_mask = cv2.resize(test_mask,(w,h)) + # test_mask = impro.ch_one2three(test_mask) + + x,y,size,area = impro.boundingSquare(mask,Ex_mul=1.5) + rat = min(orgin_image.shape[:2])/128.0 + x,y,size = int(rat*x),int(rat*y),int(rat*size) + orgin_crop = orgin_image[y-size:y+size,x-size:x+size] + mosaic_crop = mosaic_image[y-size:y+size,x-size:x+size] + # mosaic_crop = test_mask[y-size:y+size,x-size:x+size] + + result = impro.makedataset(mosaic_crop,orgin_crop) + cv2.imwrite(dir_dataset+file,result) + if i%1000==0: + print(i,'image finished.') diff --git a/make_datasets/get_video_dataset.py b/make_datasets/use_addmosaic_model_make_video_dataset.py similarity index 100% rename from make_datasets/get_video_dataset.py rename to make_datasets/use_addmosaic_model_make_video_dataset.py diff --git a/make_datasets/use_drawn_mask_make_dataset.py b/make_datasets/use_drawn_mask_make_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..faf6eac8df24d4c94dffeeb963e9ddf0870e8288 --- /dev/null +++ b/make_datasets/use_drawn_mask_make_dataset.py @@ -0,0 +1,66 @@ +import numpy as np +import cv2 +import os +from torchvision import transforms +from PIL import Image +import random +import sys +sys.path.append("..") +import util.image_processing as impro +from util import util,mosaic +import datetime +import shutil + +mask_path = '/media/hypo/Porject/Datasets/unet/av/mask' +img_path ='/media/hypo/Porject/Datasets/unet/av/origin_image' +output_dir = './datasets_img' +util.makedirs(output_dir) +HD = True # if false make dataset for pix2pix, if Ture for pix2pix_HD +MASK = False # if True, output mask,too +OUT_SIZE = 256 +FOLD_NUM = 5 + +if HD: + train_A_path = os.path.join(output_dir,'train_A') + train_B_path = os.path.join(output_dir,'train_B') + util.makedirs(train_A_path) + util.makedirs(train_B_path) +else: + train_path = os.path.join(output_dir,'train') + util.makedirs(train_path) +if MASK: + mask_path = os.path.join(output_dir,'mask') + util.makedirs(mask_path) + +mask_names = os.listdir(mask_path) +img_names = os.listdir(img_path) +mask_names.sort() +img_names.sort() +print('Find images:',len(img_names)) + +cnt = 0 +for fold in range(FOLD_NUM): + for img_name,mask_name in zip(img_names,mask_names): + try: + img = impro.imread(os.path.join(img_path,img_name)) + mask = impro.imread(os.path.join(mask_path,mask_name),'gray') + mask = impro.resize_like(mask, img) + x,y,size,area = impro.boundingSquare(mask, 1.5) + if area > 100: + img = impro.resize(img[y-size:y+size,x-size:x+size],OUT_SIZE) + mask = impro.resize(mask[y-size:y+size,x-size:x+size],OUT_SIZE) + img_mosaic = mosaic.addmosaic_random(img, mask) + + if HD: + cv2.imwrite(os.path.join(train_A_path,'%05d' % cnt+'.jpg'), img_mosaic) + cv2.imwrite(os.path.join(train_B_path,'%05d' % cnt+'.jpg'), img) + else: + merge_img = impro.makedataset(img_mosaic, img) + cv2.imwrite(os.path.join(train_path,'%05d' % cnt+'.jpg'), merge_img) + if MASK: + cv2.imwrite(os.path.join(mask_path,'%05d' % cnt+'.png'), mask) + print("Processing:",img_name," ","Remain:",len(img_names)*FOLD_NUM-cnt) + + except Exception as e: + print(img_name,e) + cnt += 1 diff --git a/make_datasets/use_irregular_holes_mask.py b/make_datasets/use_irregular_holes_mask.py deleted file mode 100644 index f00606d99cf57bc06b7d4027841d8921ff7110d7..0000000000000000000000000000000000000000 --- a/make_datasets/use_irregular_holes_mask.py +++ /dev/null @@ -1,69 +0,0 @@ -import numpy as np -import cv2 -import os -from torchvision import transforms -from PIL import Image -import random -import sys -sys.path.append("..") -import util.image_processing as impro -from util import util,mosaic -import datetime - -ir_mask_path = './Irregular_Holes_mask' -# img_path = 'D:/MyProject_new/face_512' -img_path ='/media/hypo/Hypoyun/Hypoyun/手机摄影/20190219' -output_dir = './datasets' -util.makedirs(output_dir) -HD = True #if false make dataset for pix2pix, if Ture for pix2pix_HD -MASK = True -if HD: - train_A_path = os.path.join(output_dir,'train_A') - train_B_path = os.path.join(output_dir,'train_B') - util.makedirs(train_A_path) - util.makedirs(train_B_path) -else: - train_path = os.path.join(output_dir,'train') - util.makedirs(train_path) -if MASK: - mask_path = os.path.join(output_dir,'mask') - util.makedirs(mask_path) - -transform_mask = transforms.Compose([ - transforms.RandomResizedCrop(size=512, scale=(0.5,1)), - transforms.RandomHorizontalFlip(), - ]) - -transform_img = transforms.Compose([ - - transforms.Resize(512), - transforms.RandomCrop(512) - ]) - -mask_names = os.listdir(ir_mask_path) -img_names = os.listdir(img_path) -print('Find images:',len(img_names)) - -for i,img_name in enumerate(img_names,1): - try: - img = Image.open(os.path.join(img_path,img_name)) - img = transform_img(img) - img = np.array(img) - img = img[...,::-1] - - mask = Image.open(os.path.join(ir_mask_path,random.choices(mask_names)[0])) - mask = transform_mask(mask) - mask = np.array(mask) - - mosaic_img = mosaic.addmosaic_random(img, mask) - if HD: - cv2.imwrite(os.path.join(train_A_path,'%05d' % i+'.jpg'), mosaic_img) - cv2.imwrite(os.path.join(train_B_path,'%05d' % i+'.jpg'), img) - else: - merge_img = impro.makedataset(mosaic_img, img) - cv2.imwrite(os.path.join(train_path,'%05d' % i+'.jpg'), merge_img) - if MASK: - cv2.imwrite(os.path.join(mask_path,'%05d' % i+'.png'), mask) - print("Processing:",img_name," ","Remain:",len(img_names)-i) - except Exception as e: - print(img_name,e) diff --git a/make_datasets/use_irregular_holes_mask_make_dataset.py b/make_datasets/use_irregular_holes_mask_make_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0d4bff79e32b68f9c34c3053ca06369f9e25baa6 --- /dev/null +++ b/make_datasets/use_irregular_holes_mask_make_dataset.py @@ -0,0 +1,81 @@ +import numpy as np +import cv2 +import os +from torchvision import transforms +from PIL import Image +import random +import sys +sys.path.append("..") +import util.image_processing as impro +from util import util,mosaic +import datetime + +ir_mask_path = './Irregular_Holes_mask' +img_path ='/home/hypo/桌面/FaceRankSample' +output_dir = './datasets_img' +util.makedirs(output_dir) +MOD = 'HD' #HD | pix2pix | mosaic +MASK = False # if True, output mask,too +BOUNDING = False # if true the mosaic size will be more big + +if MOD='HD': + train_A_path = os.path.join(output_dir,'train_A') + train_B_path = os.path.join(output_dir,'train_B') + util.makedirs(train_A_path) + util.makedirs(train_B_path) +elif MOD='pix2pix': + train_path = os.path.join(output_dir,'train') + util.makedirs(train_path) +if MASK: + mask_path = os.path.join(output_dir,'mask') + util.makedirs(mask_path) + +transform_mask = transforms.Compose([ + transforms.RandomResizedCrop(size=512, scale=(0.5,1)), + transforms.RandomHorizontalFlip(), + ]) + +transform_img = transforms.Compose([ + + transforms.Resize(512), + transforms.RandomCrop(512) + ]) + +mask_names = os.listdir(ir_mask_path) +img_names = os.listdir(img_path) +print('Find images:',len(img_names)) + +for i,img_name in enumerate(img_names,1): + try: + img = Image.open(os.path.join(img_path,img_name)) + img = transform_img(img) + img = np.array(img) + img = img[...,::-1] + + if BOUNDING: + mosaic_area = 0 + while mosaic_area < 16384: + mask = Image.open(os.path.join(ir_mask_path,random.choices(mask_names)[0])) + mask = transform_mask(mask) + mask = np.array(mask) + mosaic_area = impro.mask_area(mask) + mosaic_img = mosaic.addmosaic_random(img, mask,'bounding') + BOUNDING_flag = '_bound' + else: + mask = Image.open(os.path.join(ir_mask_path,random.choices(mask_names)[0])) + mask = transform_mask(mask) + mask = np.array(mask) + mosaic_img = mosaic.addmosaic_random(img, mask) + BOUNDING_flag = '' + + if HD:#[128:384,128:384,:] --->256 + cv2.imwrite(os.path.join(train_A_path,'%05d' % i+BOUNDING_flag+'.jpg'), mosaic_img) + cv2.imwrite(os.path.join(train_B_path,'%05d' % i+BOUNDING_flag+'.jpg'), img) + else: + merge_img = impro.makedataset(mosaic_img, img) + cv2.imwrite(os.path.join(train_path,'%05d' % i+BOUNDING_flag+'.jpg'), merge_img) + if MASK: + cv2.imwrite(os.path.join(mask_path,'%05d' % i+BOUNDING_flag+'.png'), mask) + print("Processing:",img_name," ","Remain:",len(img_names)-i) + except Exception as e: + print(img_name,e) diff --git a/options.py b/options.py new file mode 100644 index 0000000000000000000000000000000000000000..786070229a80865831584d6067232203b8622651 --- /dev/null +++ b/options.py @@ -0,0 +1,63 @@ +import argparse +import os + +class Options(): + def __init__(self): + self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + self.initialized = False + + def initialize(self): + + #base + self.parser.add_argument('--use_gpu', action='store_true', help='if input it, use gpu') + self.parser.add_argument('--media_path', type=str, default='./hands_test.mp4',help='your videos or images path') + self.parser.add_argument('--mode', type=str, default='auto',help='add or clean mosaic into your media auto | add | clean') + self.parser.add_argument('--model_path', type=str, default='./pretrained_models/add_hands_128.pth',help='pretrained model path') + self.parser.add_argument('--result_dir', type=str, default='./result',help='output result will be saved here') + self.parser.add_argument('--tempimage_type', type=str, default='png',help='type of temp image, png | jpg, png is better but occupy more storage space') + + #AddMosaic + self.parser.add_argument('--mosaic_mod', type=str, default='squa_avg',help='type of mosaic -> squa_avg | squa_random | squa_avg_circle_edge | rect_avg | random') + self.parser.add_argument('--mosaic_size', type=int, default=0,help='mosaic size,if 0 auto size') + self.parser.add_argument('--mask_extend', type=int, default=10,help='more mosaic area') + self.parser.add_argument('--mask_threshold', type=int, default=64,help='threshold of recognize mosaic position 0~255') + self.parser.add_argument('--output_size', type=int, default=0,help='size of output file,if 0 -> origin') + + #CleanMosaic + self.parser.add_argument('--netG', type=str, default='auto',help='select model to use for netG(clean mosaic) -> auto | unet_128 | resnet_9blocks | HD') + self.parser.add_argument('--mosaic_position_model_path', type=str, default='auto',help='name of model use to find mosaic position') + self.parser.add_argument('--no_feather', action='store_true', help='if true, no edge feather and color correction, but run faster') + self.parser.add_argument('--medfilt_num', type=int, default=11,help='medfilt window of mosaic movement in the video') + self.initialized = True + + + def getparse(self): + if not self.initialized: + self.initialize() + self.opt = self.parser.parse_args() + + if self.opt.mode == 'auto': + if 'add' in self.opt.model_path: + self.opt.mode = 'add' + elif 'clean' in self.opt.model_path: + self.opt.mode = 'clean' + else: + print('Please input running mode!') + + if self.opt.netG == 'auto' and self.opt.mode =='clean': + if 'unet_128' in self.opt.model_path: + self.opt.netG = 'unet_128' + elif 'resnet_9blocks' in self.opt.model_path: + self.opt.netG = 'resnet_9blocks' + elif 'HD' in self.opt.model_path: + self.opt.netG = 'HD' + else: + print('Type of Generator error!') + + + if self.opt.mosaic_position_model_path == 'auto': + _path = os.path.join(os.path.split(self.opt.model_path)[0],'mosaic_position.pth') + self.opt.mosaic_position_model_path = _path + # print(self.opt.mosaic_position_model_path) + + return self.opt \ No newline at end of file diff --git a/util/data.py b/util/data.py index 2396fce365cc9be811e44d01e68b92edb3143a19..ee79e058ea3189f5382b93c2cdcbd9a972a0817e 100755 --- a/util/data.py +++ b/util/data.py @@ -8,29 +8,40 @@ transform = transforms.Compose([ ] ) -def tensor2im(image_tensor, imtype=np.uint8, rgb2bgr = True): +def tensor2im(image_tensor, imtype=np.uint8, gray=False, rgb2bgr = True): image_tensor =image_tensor.data image_numpy = image_tensor[0].cpu().float().numpy() + # if gray: + # image_numpy = (image_numpy+1.0)/2.0 * 255.0 + # else: if image_numpy.shape[0] == 1: image_numpy = np.tile(image_numpy, (3, 1, 1)) image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 - if rgb2bgr: + if rgb2bgr and not gray: image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy) return image_numpy.astype(imtype) -def im2tensor(image_numpy, imtype=np.uint8, bgr2rgb = True, reshape = True, use_gpu = True, use_transform = True): - h, w ,ch = image_numpy.shape - if bgr2rgb: - image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy) - if use_transform: - image_tensor = transform(image_numpy) - else: - image_numpy = image_numpy/255.0 - image_numpy = image_numpy.transpose((2, 0, 1)) +def im2tensor(image_numpy, imtype=np.uint8, gray=False,bgr2rgb = True, reshape = True, use_gpu = True, use_transform = True): + + if gray: + h, w = image_numpy.shape + image_numpy = (image_numpy/255.0-0.5)/0.5 image_tensor = torch.from_numpy(image_numpy).float() - if reshape: - image_tensor=image_tensor.reshape(1,ch,h,w) + if reshape: + image_tensor=image_tensor.reshape(1,1,h,w) + else: + h, w ,ch = image_numpy.shape + if bgr2rgb: + image_numpy = image_numpy[...,::-1]-np.zeros_like(image_numpy) + if use_transform: + image_tensor = transform(image_numpy) + else: + image_numpy = image_numpy/255.0 + image_numpy = image_numpy.transpose((2, 0, 1)) + image_tensor = torch.from_numpy(image_numpy).float() + if reshape: + image_tensor=image_tensor.reshape(1,ch,h,w) if use_gpu: image_tensor = image_tensor.cuda() return image_tensor diff --git a/util/ffmpeg.py b/util/ffmpeg.py index 6ed6ecc7c03eea931200ddf92d559ee1cc72ead6..403a1c52983913e71e6e85a1082b510fa75a0efb 100755 --- a/util/ffmpeg.py +++ b/util/ffmpeg.py @@ -1,5 +1,7 @@ import os,json +# ffmpeg 3.4.6 + def video2image(videopath,imagepath): os.system('ffmpeg -i "'+videopath+'" -f image2 '+imagepath) @@ -15,9 +17,26 @@ def get_video_infos(videopath): cmd_str = 'ffprobe -v quiet -print_format json -show_format -show_streams -i "' + videopath + '"' out_string = os.popen(cmd_str).read() infos = json.loads(out_string) - fps = eval(infos['streams'][0]['avg_frame_rate']) - endtime = float(infos['format']['duration']) - width = int(infos['streams'][0]['width']) - height = int(infos['streams'][0]['height']) + try: + fps = eval(infos['streams'][0]['avg_frame_rate']) + endtime = float(infos['format']['duration']) + width = int(infos['streams'][0]['width']) + height = int(infos['streams'][0]['height']) + except Exception as e: + fps = eval(infos['streams'][1]['r_frame_rate']) + endtime = float(infos['format']['duration']) + width = int(infos['streams'][1]['width']) + height = int(infos['streams'][1]['height']) + return fps,endtime,width,height +def cut_video(in_path,start_time,last_time,out_path,vcodec='h265'): + if vcodec == 'copy': + os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec copy -acodec copy '+out_path) + elif vcodec == 'h264': + os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec libx264 -b 12M '+out_path) + elif vcodec == 'h265': + os.system('ffmpeg -ss '+start_time+' -t '+last_time+' -i "'+in_path+'" -vcodec libx265 -b 12M '+out_path) + +def continuous_screenshot(videopath,savedir,fps): + os.system('ffmpeg -i '+videopath+' -vf fps='+str(fps)+' '+savedir+'/'+'%05d.jpg') diff --git a/util/image_processing.py b/util/image_processing.py index c4ed2de3dce5d396de7e8a76919237b4c57dae67..eb7952c3b337e9cca58b22d3d8641bb0abe8bffe 100755 --- a/util/image_processing.py +++ b/util/image_processing.py @@ -1,10 +1,17 @@ import cv2 import numpy as np +def imread(file_path,mod = 'normal'): -# imread for chinese path in windows -def imread(file_path): - cv_img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1) + if mod == 'normal': + cv_img = cv2.imread(file_path) + elif mod == 'gray': + cv_img = cv2.imread(file_path,0) + elif mod == 'all': + cv_img = cv2.imread(file_path,-1) + + # # imread for chinese path in windows but no EXIF + # cv_img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1) return cv_img def resize(img,size): @@ -17,6 +24,11 @@ def resize(img,size): res = cv2.resize(img,(size, int(size*h/w))) return res +def resize_like(img,img_like): + h, w = img_like.shape[:2] + img = cv2.resize(img, (w,h)) + return img + def ch_one2three(img): #zeros = np.zeros(img.shape[:2], dtype = "uint8") # ret,thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY) diff --git a/util/mosaic.py b/util/mosaic.py index e2ba16de3a32fad6ccddb986b99d85d9d437b11e..fbeed3804085350351932f1c8e410bc96948b7b8 100755 --- a/util/mosaic.py +++ b/util/mosaic.py @@ -126,9 +126,9 @@ def addmosaic_random(img,mask,area_type = 'normal'): area = 0 area = area/(alpha*alpha) if area>50000: - img_mosaic = random_mod(img,mask,alpha*random.uniform(16,30)) + img_mosaic = random_mod(img,mask,alpha*random.uniform(8,30)) #16,30 elif 20000