def evaluate(hypes, sess, image_pl, inf_out):
#softmax
softmax = inf_out['softmax']
data_dir = hypes['dirs']['data_dir']
eval_dict = {}
for phase in ['train', 'val']:
data_file = hypes['data']['{}_file'.format(phase)]
data_file = os.path.join(data_dir, data_file)
image_dir = os.path.dirname(data_file)
thresh = np.array(range(0, 256))/255.0
total_fp = np.zeros(thresh.shape)
total_fn = np.zeros(thresh.shape)
total_posnum = 0
total_negnum = 0
image_list = []
with open(data_file) as file:
for i, datum in enumerate(file):
datum = datum.rstrip()
image_file, gt_file = datum.split(" ")
image_file = os.path.join(image_dir, image_file)
gt_file = os.path.join(image_dir, gt_file)
image = scp.misc.imread(image_file, mode='RGB')
gt_image = scp.misc.imread(gt_file, mode='RGB')
#accoriding to the image rescaling options specified in hypes
if hypes['jitter']['fix_shape']:
shape = image.shape
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
assert(image_height >= shape[0])
assert(image_width >= shape[1])
offset_x = (image_height - shape[0])//2
offset_y = (image_width - shape[1])//2
new_image = np.zeros([image_height, image_width, 3])
new_image[offset_x:offset_x+shape[0],
offset_y:offset_y+shape[1]] = image
input_image = new_image
elif hypes['jitter']['reseize_image']:
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
gt_image_old = gt_image
image, gt_image = resize_label_image(image, gt_image,
image_height,
image_width)
input_image = image
else:
input_image = image
shape = input_image.shape
feed_dict = {image_pl: input_image}
output = sess.run([softmax], feed_dict=feed_dict)
output_im = output[0][:, 1].reshape(shape[0], shape[1])
if hypes['jitter']['fix_shape']:
gt_shape = gt_image.shape
output_im = output_im[offset_x:offset_x+gt_shape[0],
offset_y:offset_y+gt_shape[1]]
#if this is a validation process
if phase == 'val':
# Saving RB Plot
ov_image = seg.make_overlay(image, output_im)
name = os.path.basename(image_file)
image_list.append((name, ov_image))
name2 = name.split('.')[0] + '_green.png'
hard = output_im > 0.5
green_image = utils.fast_overlay(image, hard)
image_list.append((name2, green_image))
FN, FP, posNum, negNum = eval_image(hypes,
gt_image, output_im)
total_fp += FP
total_fn += FN
total_posnum += posNum
total_negnum += negNum
eval_dict[phase] = seg.pxEval_maximizeFMeasure(
total_posnum, total_negnum, total_fn, total_fp, thresh=thresh)
if phase == 'val':
start_time = time.time()
for i in xrange(10):
sess.run([softmax], feed_dict=feed_dict)
dt = (time.time() - start_time)/10
eval_list = []
#performance measures
for phase in ['train', 'val']:
eval_list.append(('[{}] MaxF1'.format(phase),
100*eval_dict[phase]['MaxF']))
eval_list.append(('[{}] BestThresh'.format(phase),
100*eval_dict[phase]['BestThresh']))
eval_list.append(('[{}] Average Precision'.format(phase),
100*eval_dict[phase]['AvgPrec']))
eval_list.append(('Speed (msec)', 1000*dt))
eval_list.append(('Speed (fps)', 1/dt))
return eval_list, image_list
def evaluate(hypes, sess, image_pl, inf_out):
out_layer = inf_out['output']
data_dir = hypes['dirs']['data_dir']
eval_dict = {}
for phase in ['train', 'val']:
data_file = hypes['data']['{}_file'.format(phase)]
data_file = os.path.join(data_dir, data_file)
image_dir = os.path.dirname(data_file)
thresh = np.array(range(0, 256)) / 255.0
total_fp = np.zeros(thresh.shape)
total_fn = np.zeros(thresh.shape)
total_posnum = 0
total_negnum = 0
image_list = []
with open(data_file) as file:
for i, datum in enumerate(file):
datum = datum.rstrip()
image_file, gt_file = datum.split("\t")
image_file = os.path.join(image_dir, image_file)
gt_file = os.path.join(image_dir, gt_file)
# image = scp.misc.imread(image_file, mode='RGB')
# gt_image = scp.misc.imread(gt_file, mode='RGB')
image = cv2.imread(image_file, cv2.IMREAD_COLOR)
gt_image = cv2.imread(gt_file, cv2.IMREAD_COLOR)
if hypes['jitter']['fix_shape']:
shape = image.shape
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
assert (image_height >= shape[0])
assert (image_width >= shape[1])
offset_x = (image_height - shape[0]) // 2
offset_y = (image_width - shape[1]) // 2
new_image = np.zeros([image_height, image_width, 3])
new_image[offset_x:offset_x + shape[0],
offset_y:offset_y + shape[1]] = image
input_image = new_image
elif hypes['jitter']['resize_image']:
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
gt_image_old = gt_image
image, gt_image = resize_label_image(
image, gt_image, image_height, image_width)
input_image = image
else:
input_image = image
shape = input_image.shape
feed_dict = {image_pl: input_image}
output = sess.run([out_layer], feed_dict=feed_dict)
# getting an inference of object of interest location
if hypes['arch']['output'] == "softmax":
output_im = output[0][:, 1].reshape(shape[0], shape[1])
elif hypes['arch']['output'] == "sigmoid":
output_im = output[0].reshape(shape[0], shape[1])
elif hypes['arch']['output'] == "regress":
output_center = output[0]
if hypes['jitter']['fix_shape']:
gt_shape = gt_image.shape
output_im = output_im[offset_x:offset_x + gt_shape[0],
offset_y:offset_y + gt_shape[1]]
if hypes['arch']['output'] != "regress":
if phase == 'val':
# Saving RB Plot
ov_image = seg.make_overlay(image, output_im)
name = os.path.basename(image_file)
image_list.append((name, ov_image))
name2 = name.split('.')[0] + '_blue.png'
hard = output_im > 0.5
blue_image = utils.fast_overlay(image, hard, \
color=[0, 0, 255, 127])
image_list.append((name2, blue_image))
FN, FP, posNum, negNum = eval_image(
hypes, gt_image, output_im)
total_fp += FP
total_fn += FN
total_posnum += posNum
total_negnum += negNum
if hypes['arch']['output'] != "regress":
eval_dict[phase] = seg.pxEval_maximizeFMeasure(total_posnum,
total_negnum,
total_fn,
total_fp,
thresh=thresh)
if phase == 'val':
start_time = time.time()
for i in range(10):
sess.run([out_layer], feed_dict=feed_dict)
dt = (time.time() - start_time) / 10
eval_list = []
if hypes['arch']['output'] != "regress":
for phase in ['train', 'val']:
eval_list.append(
('[{}] MaxF1'.format(phase), 100 * eval_dict[phase]['MaxF']))
eval_list.append(('[{}] BestThresh'.format(phase),
100 * eval_dict[phase]['BestThresh']))
eval_list.append(('[{}] Average Precision'.format(phase),
100 * eval_dict[phase]['AvgPrec']))
eval_list.append(('Speed (msec)', 1000 * dt))
eval_list.append(('Speed (fps)', 1 / dt))
return eval_list, image_list
def evaluate(hypes, sess, image_pl, inf_out):
softmax = inf_out['softmax']
data_dir = hypes['dirs']['data_dir']
eval_dict = {}
for phase in ['train', 'val']:
data_file = hypes['data']['{}_file'.format(phase)]
data_file = os.path.join(data_dir, data_file)
image_dir = os.path.dirname(data_file)
thresh = np.array(range(0, 256))/255.0
total_fp = np.zeros(thresh.shape)
total_fn = np.zeros(thresh.shape)
total_posnum = 0
total_negnum = 0
image_list = []
with open(data_file) as file:
for i, datum in enumerate(file):
datum = datum.rstrip()
image_file, gt_file = datum.split(" ")
image_file = os.path.join(image_dir, image_file)
gt_file = os.path.join(image_dir, gt_file)
image = scp.misc.imread(image_file, mode='RGB')
gt_image = scp.misc.imread(gt_file, mode='RGB')
if hypes['jitter']['fix_shape']:
shape = image.shape
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
assert(image_height >= shape[0])
assert(image_width >= shape[1])
offset_x = (image_height - shape[0])//2
offset_y = (image_width - shape[1])//2
new_image = np.zeros([image_height, image_width, 3])
new_image[offset_x:offset_x+shape[0],
offset_y:offset_y+shape[1]] = image
input_image = new_image
elif hypes['jitter']['reseize_image']:
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
gt_image_old = gt_image
image, gt_image = resize_label_image(image, gt_image,
image_height,
image_width)
input_image = image
else:
input_image = image
shape = input_image.shape
feed_dict = {image_pl: input_image}
output = sess.run([softmax], feed_dict=feed_dict)
output_im = output[0][:, 1].reshape(shape[0], shape[1])
if hypes['jitter']['fix_shape']:
gt_shape = gt_image.shape
output_im = output_im[offset_x:offset_x+gt_shape[0],
offset_y:offset_y+gt_shape[1]]
if phase == 'val':
# Saving RB Plot
ov_image = seg.make_overlay(image, output_im)
name = os.path.basename(image_file)
image_list.append((name, ov_image))
name2 = name.split('.')[0] + '_green.png'
hard = output_im > 0.5
green_image = utils.fast_overlay(image, hard)
image_list.append((name2, green_image))
FN, FP, posNum, negNum = eval_image(hypes,
gt_image, output_im)
total_fp += FP
total_fn += FN
total_posnum += posNum
total_negnum += negNum
eval_dict[phase] = seg.pxEval_maximizeFMeasure(
total_posnum, total_negnum, total_fn, total_fp, thresh=thresh)
if phase == 'val':
start_time = time.time()
for i in xrange(10):
sess.run([softmax], feed_dict=feed_dict)
dt = (time.time() - start_time)/10
eval_list = []
for phase in ['train', 'val']:
eval_list.append(('[{}] MaxF1'.format(phase),
100*eval_dict[phase]['MaxF']))
eval_list.append(('[{}] BestThresh'.format(phase),
100*eval_dict[phase]['BestThresh']))
eval_list.append(('[{}] Average Precision'.format(phase),
100*eval_dict[phase]['AvgPrec']))
eval_list.append(('Speed (msec)', 1000*dt))
eval_list.append(('Speed (fps)', 1/dt))
return eval_list, image_list
def evaluate(hypes, sess, image_pl, inf_out):
softmax = inf_out['softmax']
data_dir = hypes['dirs']['data_dir']
num_classes = hypes['arch']['num_classes']
colors = hypes['colors']
for i, key in enumerate(colors):
colors[i] = np.array(colors[key])
del colors[key]
eval_dict = {}
for phase in ['train', 'val']:
data_file = hypes['data']['{}_file'.format(phase)]
data_file = os.path.join(data_dir, data_file)
image_dir = os.path.dirname(data_file)
thresh = np.array(range(0, 256)) / 255.0
total_fp = np.zeros(thresh.shape)
total_fn = np.zeros(thresh.shape)
total_posnum = 0
total_negnum = 0
image_list = []
with open(data_file) as file:
for i, datum in enumerate(file):
datum = datum.rstrip()
image_file, gt_file = datum.split(" ")
image_file = os.path.join(image_dir, image_file)
gt_file = os.path.join(image_dir, gt_file)
image = scp.misc.imread(image_file, mode='RGB')
gt_image = scp.misc.imread(gt_file, mode='RGB')
if hypes['jitter']['fix_shape']:
shape = image.shape
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
assert (image_height >= shape[0])
assert (image_width >= shape[1])
offset_x = (image_height - shape[0]) // 2
offset_y = (image_width - shape[1]) // 2
new_image = np.zeros([image_height, image_width, 3])
new_image[offset_x:offset_x + shape[0],
offset_y:offset_y + shape[1]] = image
input_image = new_image
elif hypes['jitter']['reseize_image']:
image_height = hypes['jitter']['image_height']
image_width = hypes['jitter']['image_width']
gt_image_old = gt_image
image, gt_image = resize_label_image(
image, gt_image, image_height, image_width)
input_image = image
else:
input_image = image
shape = input_image.shape
feed_dict = {image_pl: input_image}
output = sess.run([softmax], feed_dict=feed_dict)
output_im = output.reshape(shape[0], shape[1], num_classes)
output_im = np.argmax(output_im, axis=2)
if hypes['jitter']['fix_shape']:
gt_shape = gt_image.shape
output_im = output_im[offset_x:offset_x + gt_shape[0],
offset_y:offset_y + gt_shape[1]]
if phase == 'val':
green_image = seg.paint(output_im, colors)
name = os.path.basename(image_file)
image_list.append((name, green_image))
name2 = name.split('.')[0] + '_overlay.png'
ov_image = seg.blend_transparent(image, green_image)
image_list.append((name2, ov_image))
FN, FP, posNum, negNum = eval_image(hypes, gt_image, output_im)
total_fp += FP
total_fn += FN
total_posnum += posNum
total_negnum += negNum
eval_dict[phase] = seg.pxEval_maximizeFMeasure(total_posnum,
total_negnum,
total_fn,
total_fp,
thresh=thresh)
if phase == 'val':
start_time = time.time()
for i in xrange(10):
sess.run([softmax], feed_dict=feed_dict)
dt = (time.time() - start_time) / 10
eval_list = []
for phase in ['train', 'val']:
eval_list.append(
('[{}] MaxF1'.format(phase), 100 * eval_dict[phase]['MaxF']))
eval_list.append(('[{}] BestThresh'.format(phase),
100 * eval_dict[phase]['BestThresh']))
eval_list.append(('[{}] Average Precision'.format(phase),
100 * eval_dict[phase]['AvgPrec']))
eval_list.append(('Speed (msec)', 1000 * dt))
eval_list.append(('Speed (fps)', 1 / dt))
return eval_list, image_list
