From 35db7a050de73ada761ad0ac3496e8a441bb0880 Mon Sep 17 00:00:00 2001 From: Varun Chhangani Date: Thu, 14 May 2020 18:21:48 +0530 Subject: [PATCH 1/2] add heatmap --- SimpleNet/test.py | 99 ++++++++++++++++++++----------------- SimpleNet/utils.py | 67 +++++++++++++++++-------- heatmap_results/0032.png | Bin 0 -> 124936 bytes heatmap_results/220921.jpg | Bin 0 -> 77422 bytes results/0032.png | Bin 196993 -> 196993 bytes results/220921.jpg | Bin 61052 -> 77318 bytes 6 files changed, 100 insertions(+), 66 deletions(-) create mode 100644 heatmap_results/0032.png create mode 100644 heatmap_results/220921.jpg diff --git a/SimpleNet/test.py b/SimpleNet/test.py index 00483d6..2093abb 100644 --- a/SimpleNet/test.py +++ b/SimpleNet/test.py @@ -1,5 +1,19 @@ +import cv2 +import numpy as np +from utils import * +from tqdm import tqdm +from loss import * +from dataloader import TestLoader, SaliconDataset +from scipy.stats import multivariate_normal +import torch.nn.functional as F +import torch.nn.init as init +from torch.utils.data import DataLoader +from PIL import Image +from torchvision import transforms, utils +import matplotlib.pyplot as plt import argparse -import glob, os +import glob +import os import torch import sys import time @@ -9,31 +23,20 @@ from torch.autograd import Variable import matplotlib matplotlib.use('Agg') -import matplotlib.pyplot as plt -from torchvision import transforms, utils -from PIL import Image -from torch.utils.data import DataLoader -import numpy as np, cv2 -import torch -import torch.nn as nn -import torch.nn.init as init -import torch.nn.functional as F -from scipy.stats import multivariate_normal -from dataloader import TestLoader, SaliconDataset -from loss import * -from tqdm import tqdm -from utils import * parser = argparse.ArgumentParser() -parser.add_argument('--val_img_dir',default="../images/", type=str) -parser.add_argument('--model_val_path',default="../saved_models/salicon_pnas.pt", type=str) -parser.add_argument('--no_workers',default=4, type=int) -parser.add_argument('--enc_model',default="pnas", type=str) -parser.add_argument('--results_dir',default="../results/", type=str) -parser.add_argument('--validate',default=0, type=int) -parser.add_argument('--save_results',default=1, type=int) -parser.add_argument('--dataset_dir',default="/home/samyak/old_saliency/saliency/SALICON_NEW/", type=str) +parser.add_argument('--val_img_dir', default="../images/", type=str) +parser.add_argument('--model_val_path', + default="../saved_models/salicon_pnas.pt", type=str) +parser.add_argument('--no_workers', default=4, type=int) +parser.add_argument('--enc_model', default="pnas", type=str) +parser.add_argument('--results_dir', default="../results/", type=str) +parser.add_argument('--heatmap_dir', default="../heatmap_results/", type=str) +parser.add_argument('--validate', default=0, type=int) +parser.add_argument('--save_results', default=1, type=int) +parser.add_argument( + '--dataset_dir', default="/home/samyak/old_saliency/saliency/SALICON_NEW/", type=str) args = parser.parse_args() @@ -54,7 +57,7 @@ print("ResNet Model") from model import ResNetModel model = ResNetModel() - + elif args.enc_model == "vgg": print("VGG Model") from model import VGGModel @@ -65,7 +68,7 @@ from model import MobileNetV2 model = MobileNetV2() -if args.enc_model!="mobilenet": +if args.enc_model != "mobilenet": model = nn.DataParallel(model) model.load_state_dict(torch.load(args.model_val_path)) @@ -73,7 +76,9 @@ val_img_ids = os.listdir(args.val_img_dir) val_dataset = TestLoader(args.val_img_dir, val_img_ids) -vis_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=args.no_workers) +vis_loader = torch.utils.data.DataLoader( + val_dataset, batch_size=1, shuffle=False, num_workers=args.no_workers) + def validate(model, loader, device, args): model.eval() @@ -83,37 +88,41 @@ def validate(model, loader, device, args): kldiv_loss = AverageMeter() nss_loss = AverageMeter() sim_loss = AverageMeter() - + for (img, gt, fixations) in tqdm(loader): img = img.to(device) gt = gt.to(device) fixations = fixations.to(device) - + pred_map = model(img) # Blurring blur_map = pred_map.cpu().squeeze(0).clone().numpy() blur_map = blur(blur_map).unsqueeze(0).to(device) - - cc_loss.update(cc(blur_map, gt)) - kldiv_loss.update(kldiv(blur_map, gt)) - nss_loss.update(nss(blur_map, gt)) - sim_loss.update(similarity(blur_map, gt)) - print('CC : {:.5f}, KLDIV : {:.5f}, NSS : {:.5f}, SIM : {:.5f} time:{:3f} minutes'.format(cc_loss.avg, kldiv_loss.avg, nss_loss.avg, sim_loss.avg, (time.time()-tic)/60)) + cc_loss.update(cc(blur_map, gt)) + kldiv_loss.update(kldiv(blur_map, gt)) + nss_loss.update(nss(blur_map, gt)) + sim_loss.update(similarity(blur_map, gt)) + + print('CC : {:.5f}, KLDIV : {:.5f}, NSS : {:.5f}, SIM : {:.5f} time:{:3f} minutes'.format( + cc_loss.avg, kldiv_loss.avg, nss_loss.avg, sim_loss.avg, (time.time()-tic)/60)) sys.stdout.flush() - + return cc_loss.avg + if args.validate: - val_img_dir = args.dataset_dir + "images/val/" - val_gt_dir = args.dataset_dir + "maps/val/" - val_fix_dir = args.dataset_dir + "fixations/fixations/" - - val_img_ids = [nm.split(".")[0] for nm in os.listdir(val_img_dir)] - val_dataset = SaliconDataset(val_img_dir, val_gt_dir, val_fix_dir, val_img_ids) - val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=args.no_workers) - with torch.no_grad(): - validate(model, val_loader, device, args) + val_img_dir = args.dataset_dir + "images/val/" + val_gt_dir = args.dataset_dir + "maps/val/" + val_fix_dir = args.dataset_dir + "fixations/fixations/" + + val_img_ids = [nm.split(".")[0] for nm in os.listdir(val_img_dir)] + val_dataset = SaliconDataset( + val_img_dir, val_gt_dir, val_fix_dir, val_img_ids) + val_loader = torch.utils.data.DataLoader( + val_dataset, batch_size=1, shuffle=False, num_workers=args.no_workers) + with torch.no_grad(): + validate(model, val_loader, device, args) if args.save_results: - visualize_model(model, vis_loader, device, args) + visualize_model(model, vis_loader, device, args) diff --git a/SimpleNet/utils.py b/SimpleNet/utils.py index 72d843c..2bed375 100644 --- a/SimpleNet/utils.py +++ b/SimpleNet/utils.py @@ -1,4 +1,5 @@ -import cv2, os +import cv2 +import os import torch from os.path import join import matplotlib.pyplot as plt @@ -7,16 +8,18 @@ from tqdm import tqdm from PIL import Image + def blur(img): k_size = 11 - bl = cv2.GaussianBlur(img,(k_size,k_size),0) + bl = cv2.GaussianBlur(img, (k_size, k_size), 0) return torch.FloatTensor(bl) + def plot(pred, gt, args, idx): pred_npimg = utils.make_grid(pred.cpu()).numpy() gt_npimg = utils.make_grid(gt.cpu()).numpy() - plt.figure(figsize=(10,10)) + plt.figure(figsize=(10, 10)) plt.subplot(121) plt.title("Original") plt.imshow(np.transpose(gt_npimg, (1, 2, 0))) @@ -25,38 +28,55 @@ def plot(pred, gt, args, idx): plt.title("Predicted") plt.imshow(np.transpose(pred_npimg, (1, 2, 0))) - plt.savefig(args.results_dir + '{}_{}.png'.format(epoch, idx+1)) + def visualize_model(model, loader, device, args): with torch.no_grad(): model.eval() os.makedirs(args.results_dir, exist_ok=True) - + for (img, img_id, sz) in tqdm(loader): img = img.to(device) - + pred_map = model(img) pred_map = pred_map.cpu().squeeze(0).numpy() pred_map = cv2.resize(pred_map, (sz[0], sz[1])) - + pred_map = torch.FloatTensor(blur(pred_map)) - img_save(pred_map, join(args.results_dir, img_id[0]), normalize=True) + img_save(pred_map, join( + args.results_dir, img_id[0]), normalize=True) + + heat_map = im2heat(join(args.results_dir, img_id[0]), join( + args.val_img_dir, img_id[0])) + + im = Image.fromarray(heat_map) + exten = join(args.heatmap_dir, img_id[0]).split('.')[-1] + if exten == "png": + im.save(join(args.heatmap_dir, + img_id[0]), format=format, compress_level=0) + else: + # for jpg + im.save(join(args.heatmap_dir, + img_id[0]), format=format, quality=100) + def img_save(tensor, fp, nrow=8, padding=2, - normalize=False, range=None, scale_each=False, pad_value=0, format=None): + normalize=False, range=None, scale_each=False, pad_value=0, format=None): grid = utils.make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value, - normalize=normalize, range=range, scale_each=scale_each) + normalize=normalize, range=range, scale_each=scale_each) ''' Add 0.5 after unnormalizing to [0, 255] to round to nearest integer ''' - - ndarr = torch.round(grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0)).to('cpu', torch.uint8).numpy() + + ndarr = torch.round(grid.mul(255).add_(0.5).clamp_( + 0, 255).permute(1, 2, 0)).to('cpu', torch.uint8).numpy() im = Image.fromarray(ndarr) exten = fp.split('.')[-1] - if exten=="png": + if exten == "png": im.save(fp, format=format, compress_level=0) else: - im.save(fp, format=format, quality=100) #for jpg + im.save(fp, format=format, quality=100) # for jpg + class AverageMeter(object): @@ -72,18 +92,23 @@ def reset(self): self.sum = 0 self.count = 0 - def update(self, val, n = 1): + def update(self, val, n=1): self.val = val self.sum += val*n self.count += n self.avg = self.sum / self.count -def im2heat(pred_dir, a, gt, exten='.png'): - pred_nm = pred_dir + a + exten - pred = cv2.imread(pred_nm, 0) + +def convert(img): + return cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + +def im2heat(map_path, rgb_image_path): + gt = cv2.imread(rgb_image_path) + gt = convert(gt) + pred = cv2.imread(map_path, 0) heatmap_img = cv2.applyColorMap(pred, cv2.COLORMAP_JET) heatmap_img = convert(heatmap_img) - pred = np.stack((pred, pred, pred),2).astype('float32') + pred = np.stack((pred, pred, pred), 2).astype('float32') pred = pred / 255.0 - - return np.uint8(pred * heatmap_img + (1.0-pred) * gt) \ No newline at end of file + return np.uint8(pred * heatmap_img + (1.0-pred) * gt) diff --git a/heatmap_results/0032.png b/heatmap_results/0032.png new file mode 100644 index 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z^-&c;wCRxwA!oDygIBGv-XG%8L(mYd>q%Prn`7&o|6Wxm&6QRk9cQs7*PQ>XcS@x# zEh8EAI5Xmz?`_Tlhgy6&8TC7CE;04z??@i%p6^Q;N!RgGaz9uZbKu`=asD@by=qS? z7N%Y8EAjo^@81E9ngYw}+`B_IchSD2%N2RII(v+ayFzGwQBA`BL&FlsS#JGz|B(kf zNY@V6B2I&$8rUe_sRSzdiduq(DCy From a9edbd7d89d1e170bfb5056eb48e7a103d489995 Mon Sep 17 00:00:00 2001 From: Varun Chhangani Date: Fri, 15 May 2020 19:52:00 +0530 Subject: [PATCH 2/2] small error --- SimpleNet/utils.py | 4 ++-- heatmap_results/0032.png | Bin 124936 -> 196993 bytes heatmap_results/220921.jpg | Bin 77422 -> 270371 bytes 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/SimpleNet/utils.py b/SimpleNet/utils.py index 2bed375..af8295a 100644 --- a/SimpleNet/utils.py +++ b/SimpleNet/utils.py @@ -54,11 +54,11 @@ def visualize_model(model, loader, device, args): exten = join(args.heatmap_dir, img_id[0]).split('.')[-1] if exten == "png": im.save(join(args.heatmap_dir, - img_id[0]), format=format, compress_level=0) + img_id[0]), format=None, compress_level=0) else: # for 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