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import matplotlib
matplotlib.use('Agg')
import math
import torch
import copy
import time
from torch.autograd import Variable
import shutil
from skimage import io
import numpy as np
from utils.utils import fan_NME, show_landmarks, get_preds_fromhm
from PIL import Image, ImageDraw
import os
import sys
import cv2
import matplotlib.pyplot as plt
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def eval_model(model, dataloaders, dataset_sizes,
writer, use_gpu=True, epoches=5, dataset='val',
save_path='./', num_landmarks=68):
global_nme = 0
model.eval()
for epoch in range(epoches):
running_loss = 0
step = 0
total_nme = 0
total_count = 0
fail_count = 0
nmes = []
# running_corrects = 0
# Iterate over data.
with torch.no_grad():
for data in dataloaders[dataset]:
total_runtime = 0
run_count = 0
step_start = time.time()
step += 1
# get the inputs
inputs = data['image'].type(torch.FloatTensor)
labels_heatmap = data['heatmap'].type(torch.FloatTensor)
labels_boundary = data['boundary'].type(torch.FloatTensor)
landmarks = data['landmarks'].type(torch.FloatTensor)
loss_weight_map = data['weight_map'].type(torch.FloatTensor)
# wrap them in Variable
if use_gpu:
inputs = inputs.to(device)
labels_heatmap = labels_heatmap.to(device)
labels_boundary = labels_boundary.to(device)
loss_weight_map = loss_weight_map.to(device)
else:
inputs, labels_heatmap = Variable(inputs), Variable(labels_heatmap)
labels_boundary = Variable(labels_boundary)
labels = torch.cat((labels_heatmap, labels_boundary), 1)
single_start = time.time()
outputs, boundary_channels = model(inputs)
single_end = time.time()
total_runtime += time.time() - single_start
run_count += 1
step_end = time.time()
for i in range(inputs.shape[0]):
print(inputs.shape)
img = inputs[i]
img = img.cpu().numpy()
img = img.transpose((1, 2, 0)) #*255.0
# img = img.astype(np.uint8)
# img = Image.fromarray(img)
# pred_heatmap = outputs[-1][i].detach().cpu()[:-1, :, :]
pred_heatmap = outputs[-1][:, :-1, :, :][i].detach().cpu()
pred_landmarks, _ = get_preds_fromhm(pred_heatmap.unsqueeze(0))
pred_landmarks = pred_landmarks.squeeze().numpy()
gt_landmarks = data['landmarks'][i].numpy()
print(pred_landmarks, gt_landmarks)
import cv2
while(True):
imgshow = vis_landmark_on_img(cv2.UMat(img), pred_landmarks*4)
cv2.imshow('img', imgshow)
if(cv2.waitKey(10) == ord('q')):
break
if num_landmarks == 68:
left_eye = np.average(gt_landmarks[36:42], axis=0)
right_eye = np.average(gt_landmarks[42:48], axis=0)
norm_factor = np.linalg.norm(left_eye - right_eye)
# norm_factor = np.linalg.norm(gt_landmarks[36]- gt_landmarks[45])
elif num_landmarks == 98:
norm_factor = np.linalg.norm(gt_landmarks[60]- gt_landmarks[72])
elif num_landmarks == 19:
left, top = gt_landmarks[-2, :]
right, bottom = gt_landmarks[-1, :]
norm_factor = math.sqrt(abs(right - left)*abs(top-bottom))
gt_landmarks = gt_landmarks[:-2, :]
elif num_landmarks == 29:
# norm_factor = np.linalg.norm(gt_landmarks[8]- gt_landmarks[9])
norm_factor = np.linalg.norm(gt_landmarks[16]- gt_landmarks[17])
single_nme = (np.sum(np.linalg.norm(pred_landmarks*4 - gt_landmarks, axis=1)) / pred_landmarks.shape[0]) / norm_factor
nmes.append(single_nme)
total_count += 1
if single_nme > 0.1:
fail_count += 1
if step % 10 == 0:
print('Step {} Time: {:.6f} Input Mean: {:.6f} Output Mean: {:.6f}'.format(
step, step_end - step_start,
torch.mean(labels),
torch.mean(outputs[0])))
# gt_landmarks = landmarks.numpy()
# pred_heatmap = outputs[-1].to('cpu').numpy()
gt_landmarks = landmarks
batch_nme = fan_NME(outputs[-1][:, :-1, :, :].detach().cpu(), gt_landmarks, num_landmarks)
# batch_nme = 0
total_nme += batch_nme
epoch_nme = total_nme / dataset_sizes['val']
global_nme += epoch_nme
nme_save_path = os.path.join(save_path, 'nme_log.npy')
np.save(nme_save_path, np.array(nmes))
print('NME: {:.6f} Failure Rate: {:.6f} Total Count: {:.6f} Fail Count: {:.6f}'.format(epoch_nme, fail_count/total_count, total_count, fail_count))
print('Evaluation done! Average NME: {:.6f}'.format(global_nme/epoches))
print('Everage runtime for a single batch: {:.6f}'.format(total_runtime/run_count))
return model
def vis_landmark_on_img(img, shape, linewidth=2):
'''
Visualize landmark on images.
'''
def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth):
for i in idx_list:
cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth)
if (loop):
cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]),
(shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth)
draw_curve(list(range(0, 32))) # jaw
draw_curve(list(range(33, 41)), color=(0, 0, 255), loop=True) # eye brow
draw_curve(list(range(42, 50)), color=(0, 0, 255), loop=True)
draw_curve(list(range(51, 59))) # nose
draw_curve(list(range(60, 67)), loop=True) # eyes
draw_curve(list(range(68, 75)), loop=True)
draw_curve(list(range(76, 87)), loop=True, color=(0, 255, 255)) # mouth
draw_curve(list(range(88, 95)), loop=True, color=(255, 255, 0))
return img |