def confusion_matrix(results_dir, masks_dir):
"""
Calculate confusion matrix.
:param results_dir: Directory with calculated masks
:param masks_dir: Ground truth masks
:return: Confusion matrix
"""
# Getting calculated masks
result_imgs = get_files_from_dir(results_dir)
# List with values TP, FP, FN, TN
tf_values = np.zeros(4)
# Iterate over image paths
for img_path in result_imgs:
# Convert image path to mask path
mask_path = img_name_to_mask_name(img_path)
# Compute perfomance measures
tf_val = np.array(
performance_accumulation_pixel(get_img(results_dir, img_path),
get_img(masks_dir, mask_path)))
# Add them up
tf_values += tf_val
return tf_values.tolist()
def validate(analysis, dataset_manager, pixel_methods):
"""In each job, the methods are executed with the same dataset split and their results are put in an array."""
results = []
train, verify = dataset_manager.get_data_splits()
if analysis is True:
data_analysis(train)
for pixel_method in pixel_methods:
tp = 0
fn = 0
fp = 0
tn = 0
time = 0
pixel_method.train(train)
start = timer()
for dat in verify:
im = dat.get_img()
mask, im = pixel_method.get_mask(im)
mask_solution = dat.get_mask_img()
[local_tp, local_fp, local_fn, local_tn
] = evalf.performance_accumulation_pixel(mask, mask_solution)
tp += local_tp
fp += local_fp
fn += local_fn
tn += local_tn
time += timer() - start
results.append(
Result(tp=tp, fp=fp, fn=fn, tn=tn, time=(time / len(verify))))
return results
def validateMethod(train: List[Data], verify: List[Data], method):
method.train(train)
tp = 0
fn = 0
fp = 0
tn = 0
t = 0
tp_w = 0
fn_w = 0
fp_w = 0
for pos, dat in enumerate(verify):
print(method, str(pos) + '/' + str(len(verify)))
im = dat.get_img()
start = time.time()
regions, mask, im = method.get_mask(im)
mask_solution = dat.get_mask_img()
t += time.time() - start
[local_tp, local_fp, local_fn, local_tn] = evalf.performance_accumulation_pixel(
mask, mask_solution)
[local_tp_w, local_fn_w, local_fp_w] = performance_accumulation_window(regions, dat.gt)
tp += local_tp
fp += local_fp
fn += local_fn
tn += local_tn
tp_w += local_tp_w
fn_w += local_fn_w
fp_w += local_fp_w
"""import matplotlib.pyplot as plt
for region in regions:
cv2.rectangle(mask, (region.top_left[1], region.top_left[0]),
(region.get_bottom_right()[1], region.get_bottom_right()[0]), (255,), thickness=5)
for gt in dat.gt:
cv2.rectangle(mask, (gt.top_left[1], gt.top_left[0]),
(gt.get_bottom_right()[1], gt.get_bottom_right()[0]), (128,), thickness=5)
plt.imshow(mask, 'gray', vmax=255)
plt.title(cv2.countNonZero(mask))
plt.show()
pass"""
return Result(
tp=tp,
fp=fp,
fn=fn,
tn=tn,
time=(t / len(verify)),
tp_w=tp_w,
fn_w=fn_w,
fp_w=fp_w
)
def score_pixel_masks(result_masks, test_masks):
pixelTP = 0
pixelFN = 0
pixelFP = 0
pixelTN = 0
for ii, mask_name in enumerate(result_masks):
# Read mask file
pixelCandidates = imageio.imread(mask_name) > 0
if len(pixelCandidates.shape) == 3:
pixelCandidates = pixelCandidates[:, :, 0]
# Accumulate pixel performance of the current image %%%%%%%%%%%%%%%%%
gt_mask_name = test_masks[ii]
pixelAnnotation = imageio.imread(gt_mask_name) > 0
if len(pixelAnnotation.shape) == 3:
pixelAnnotation = pixelAnnotation[:, :, 0]
if pixelAnnotation.shape != pixelCandidates.shape:
print(
'Error: hypothesis ({}) and GT masks ({})dimensions do not match!'
.format(pixelCandidates.shape, pixelAnnotation.shape))
sys.exit()
[localPixelTP, localPixelFP, localPixelFN, localPixelTN
] = evalf.performance_accumulation_pixel(pixelCandidates,
pixelAnnotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
[pixelPrecision, pixelAccuracy, pixelSpecificity, pixelSensitivity
] = evalf.performance_evaluation_pixel(pixelTP, pixelFP, pixelFN, pixelTN)
pixelF1 = 0
if (pixelPrecision + pixelSensitivity) != 0:
pixelF1 = 2 * ((pixelPrecision * pixelSensitivity) /
(pixelPrecision + pixelSensitivity))
return pixelPrecision, pixelSensitivity, pixelF1
def segmentate(im_directory, mask_directory, maskOut_directory):
file_names = sorted(fnmatch.filter(os.listdir(im_directory), '*.jpg'))
pixelTP = 0
pixelFP = 0
pixelFN = 0
pixelTN = 0
from main import CONSOLE_ARGUMENTS
nf = CONSOLE_ARGUMENTS.numFiles
#For each file
for name in file_names[:nf]:
base, extension = os.path.splitext(name)
imageNameFile = im_directory + "/" + name
maskNameFile = mask_directory + "/mask." + base + ".png"
print(imageNameFile)
image = cv.imread(imageNameFile)
maskImage = cv.imread(maskNameFile)
# image = grayWorld(image)
msk = LabFilter(image)
img = image * msk
msk = msk.astype(float)
cv.imshow("masked image", img)
cv.imshow("original image", image)
cv.waitKey(0)
# cv.imwrite(os.path.join(maskOut_directory,("mask." + base + ".png")),msk)
pTP, pFP, pFN, pTN = performance_accumulation_pixel(msk, maskImage)
pixelTP += pTP
pixelFP += pFP
pixelFN += pFN
pixelTN += pTN
print("precision \t accuracy \t specificity \t sensitivity")
print(performance_evaluation_pixel(pixelTP, pixelFP, pixelFN, pixelTN))
def confusion_matrix(results_dir, masks_dir):
"""
Calculate confusion matrix.
:param results_dir: Directory with calculated masks
:param masks_dir: Ground truth masks
:return: Confusion matrix
"""
# Getting calculated masks
#result_imgs = get_files_from_dir(results_dir)
result_imgs = glob.glob(results_dir + "/*.png")
# List with values TP, FP, FN, TN
tf_values = np.zeros(4)
# Iterate over image paths
for img_path in result_imgs:
print(img_path)
image_name = img_path.split("/")[-1]
# Convert image path to mask path
# mask_path = img_name_to_mask_name(img_path) if not img_path.startswith('mask.') else img_path
mask_path = os.path.join(masks_dir, image_name)
print(mask_path)
# Compute perfomance measures
tf_val = np.array(
performance_accumulation_pixel(
#get_img(results_dir, img_path),
#get_img(masks_dir, mask_path)
imageio.imread(img_path),
imageio.imread(mask_path)))
# Add them up
tf_values += tf_val
return tf_values.tolist()
for ii in range(len(result_files)):
# Read mask file
candidate_masks_name = '{}/{}/{}'.format(results_dir, method, result_files[ii])
print ('File: {}'.format(candidate_masks_name), file = sys.stderr)
pixelCandidates = imageio.imread(candidate_masks_name)>0
if len(pixelCandidates.shape) == 3:
pixelCandidates = pixelCandidates[:,:,0]
# Accumulate pixel performance of the current image %%%%%%%%%%%%%%%%%
name, ext = os.path.splitext(test_files[ii])
gt_mask_name = '{}/mask/mask.{}.png'.format(test_dir, name)
pixelAnnotation = imageio.imread(gt_mask_name)>0
[localPixelTP, localPixelFP, localPixelFN, localPixelTN] = evalf.performance_accumulation_pixel(pixelCandidates, pixelAnnotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
if window_evaluation == 1:
# Read .pkl file
name_r, ext_r = os.path.splitext(result_files[ii])
pkl_name = '{}/{}/{}.pkl'.format(results_dir, method, name_r)
with open(pkl_name, "rb") as fp: # Unpickling
windowCandidates = pickle.load(fp)
def traffic_sign_detection(directory, output_dir, pixel_method, window_method):
pixelTP = 0
pixelFN = 0
pixelFP = 0
pixelTN = 0
windowTP = 0
windowFN = 0
windowFP = 0
window_precision = 0
window_accuracy = 0
# Load image names in the given directory
file_names = sorted(fnmatch.filter(os.listdir(directory), '*.jpg'))
pixel_time = 0
for name in file_names:
base, extension = os.path.splitext(name)
# Read file
im = imageio.imread('{}/{}'.format(directory, name))
print('{}/{}'.format(directory, name))
# Candidate Generation (pixel) ######################################
start = time.time()
pixel_candidates = candidate_generation_pixel(im, pixel_method)
end = time.time()
pixel_time += (end - start)
fd = '{}/{}_{}'.format(output_dir, pixel_method, window_method)
if not os.path.exists(fd):
os.makedirs(fd)
out_mask_name = '{}/{}.png'.format(fd, base)
imageio.imwrite(out_mask_name, np.uint8(np.round(pixel_candidates)))
if window_method != 'None':
window_candidates = candidate_generation_window(
im, pixel_candidates, window_method)
out_list_name = '{}/{}.pkl'.format(fd, base)
with open(out_list_name, "wb") as fp: #Pickling
pickle.dump(window_candidates, fp)
# Accumulate pixel performance of the current image #################
pixel_annotation = imageio.imread('{}/mask/mask.{}.png'.format(
directory, base)) > 0
[localPixelTP, localPixelFP, localPixelFN, localPixelTN
] = evalf.performance_accumulation_pixel(pixel_candidates,
pixel_annotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
[
pixel_precision, pixel_accuracy, pixel_recall, pixel_specificity,
pixel_sensitivity, pixel_F1, pixel_TP, pixel_FP, pixel_FN
] = evalf.performance_evaluation_pixel(pixelTP, pixelFP, pixelFN,
pixelTN)
if window_method != 'None':
# Accumulate object performance of the current image ################
window_annotationss = load_annotations('{}/gt/gt.{}.txt'.format(
directory, base))
[localWindowTP, localWindowFN, localWindowFP
] = evalf.performance_accumulation_window(window_candidates,
window_annotationss)
windowTP = windowTP + localWindowTP
windowFN = windowFN + localWindowFN
windowFP = windowFP + localWindowFP
# Plot performance evaluation
[window_precision, window_sensitivity, window_accuracy
] = evalf.performance_evaluation_window(windowTP, windowFN,
windowFP)
pixel_time /= len(file_names)
return [
pixel_precision, pixel_accuracy, pixel_recall, pixel_specificity,
pixel_sensitivity, pixel_F1, pixel_TP, pixel_FP, pixel_FN, pixel_time
]
def traffic_sign_detection_test(directory, output_dir, pixel_method,
window_method):
"""
Calculates all statistical evaluation metrics of different pixel selector method (TRAINING AND VALIDATION)
* Inputs:
- directory = path to train images
- outpit_dir = Directory where to store output masks, etc. For instance '~/m1-results/week1/test'
- pixel_method = pixel method that will segmentate the image
- window_method = -------
*Outputs:
- pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity, window_precision, window_accuracy
"""
from main import CONSOLE_ARGUMENTS
pixelTP = 0
pixelFN = 0
pixelFP = 0
pixelTN = 0
windowTP = 0
windowFN = 0
windowFP = 0
window_precision = 0
window_accuracy = 0
print("splitting in trainning test")
# Load image names in the given directory
file_names = sorted(fnmatch.filter(os.listdir(directory), '*.jpg'))
signals_type_dict = get_dictionary()
training, validation = [], []
for key in signals_type_dict:
sig_subdict = signals_type_dict[key]
training_type, validation_type = divide_training_validation_SL(
sig_subdict['signal_list'])
training.extend(training_type)
validation.extend(validation_type)
print("extracting mask")
dataset = training
if (CONSOLE_ARGUMENTS.use_validation):
dataset = validation
# if(CONSOLE_ARGUMENTS.use_test):
totalTime = 0
for signal in dataset:
signal_path = signal.img_orig_path
_, name = signal_path.rsplit('/', 1)
base, extension = os.path.splitext(name)
# Read file
im = imageio.imread('{}/{}'.format(directory, name))
print('{}/{}'.format(directory, name))
# Candidate Generation (pixel) ######################################
start = time.time()
pixel_candidates = candidate_generation_pixel(im, pixel_method)
totalTime += time.time() - start
fd = '{}/{}_{}'.format(output_dir, pixel_method, window_method)
if not os.path.exists(fd):
os.makedirs(fd)
out_mask_name = '{}/{}.png'.format(fd, base)
imageio.imwrite(out_mask_name, np.uint8(np.round(pixel_candidates)))
if window_method != 'None':
window_candidates = candidate_generation_window(
im, pixel_candidates, window_method)
# Accumulate pixel performance of the current image #################
pixel_annotation = imageio.imread('{}/mask/mask.{}.png'.format(
directory, base)) > 0
[localPixelTP, localPixelFP, localPixelFN, localPixelTN
] = evalf.performance_accumulation_pixel(pixel_candidates,
pixel_annotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
[
pixel_precision, pixel_accuracy, pixel_specificity,
pixel_sensitivity
] = evalf.performance_evaluation_pixel(pixelTP, pixelFP, pixelFN,
pixelTN)
if window_method != 'None':
# Accumulate object performance of the current image ################
window_annotationss = load_annotations('{}/gt/gt.{}.txt'.format(
directory, base))
[localWindowTP, localWindowFN, localWindowFP
] = evalf.performance_accumulation_window(window_candidates,
window_annotationss)
windowTP = windowTP + localWindowTP
windowFN = windowFN + localWindowFN
windowFP = windowFP + localWindowFP
# Plot performance evaluation
[window_precision, window_sensitivity, window_accuracy
] = evalf.performance_evaluation_window(windowTP, windowFN,
windowFP)
print("meanTime", totalTime / len(dataset))
print("pixelTP", pixelTP, "\t", pixelFP, "\t", pixelFN)
return [
pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity,
window_precision, window_accuracy
]
def traffic_sign_detection(directory, output_dir, pixel_method, window_method,
calculate_metrics):
pixelTP = 0
pixelFN = 0
pixelFP = 0
pixelTN = 0
pixel_F1 = 0
windowTP = 0
windowFN = 0
windowFP = 0
window_precision = 0
window_accuracy = 0
window_F1 = 0
counter = 0
# Load image names in the given directory
file_names = sorted(fnmatch.filter(os.listdir(directory), '*.jpg'))
for name in file_names:
counter += 1
base, extension = os.path.splitext(name)
# Read file
im = imageio.imread('{}/{}'.format(directory, name))
#print ('{}/{}'.format(directory,name))
# Candidate Generation (pixel) ######################################
pixel_candidates = candidate_generation_pixel(im, pixel_method)
# pixel_candidates = morphological_operators(pixel_candidates)
pixel_candidates = connected_labels_pixel_cand(im, pixel_candidates)
fd = '{}/{}_{}'.format(output_dir, pixel_method, window_method)
if not os.path.exists(fd):
os.makedirs(fd)
out_mask_name = '{}/{}.png'.format(fd, base)
if window_method != 'None':
window_candidates = candidate_generation_window(
im, pixel_candidates, window_method)
window_mask = np.zeros(pixel_candidates.shape)
for window_candidate in window_candidates:
window_mask[window_candidate[0]:window_candidate[2],
window_candidate[1]:
window_candidate[3]] = pixel_candidates[
window_candidate[0]:window_candidate[2],
window_candidate[1]:window_candidate[3]]
out_list_name = '{}/{}.pkl'.format(fd, base)
pixel_candidates = window_mask
with open(out_list_name, "wb") as fp: #Pickling
pickle.dump(window_candidates, fp)
imageio.imwrite(out_mask_name, np.uint8(np.round(pixel_candidates)))
pixel_precision = 0
pixel_accuracy = 0
pixel_specificity = 0
pixel_sensitivity = 0
window_precision = 0
window_accuracy = 0
if (calculate_metrics):
# Accumulate pixel performance of the current image #################
pixel_annotation = imageio.imread('{}/mask/mask.{}.png'.format(
directory, base)) > 0
[localPixelTP, localPixelFP, localPixelFN, localPixelTN
] = evalf.performance_accumulation_pixel(pixel_candidates,
pixel_annotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
# [pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity, pixel_F1] = evalf.performance_evaluation_pixel(pixelTP, pixelFP, pixelFN, pixelTN)
if window_method != 'None':
# Accumulate object performance of the current image ################
window_annotationss = load_annotations(
'{}/gt/gt.{}.txt'.format(directory, base))
[localWindowTP, localWindowFN,
localWindowFP] = evalf.performance_accumulation_window(
window_candidates, window_annotationss)
windowTP = windowTP + localWindowTP
windowFN = windowFN + localWindowFN
windowFP = windowFP + localWindowFP
# Plot performance evaluation
# [window_precision, window_sensitivity, window_accuracy, window_F1] = evalf.performance_evaluation_window(windowTP, windowFN, windowFP)
if (calculate_metrics):
[
pixel_precision, pixel_accuracy, pixel_specificity,
pixel_sensitivity, pixel_F1
] = evalf.performance_evaluation_pixel(pixelTP, pixelFP, pixelFN,
pixelTN)
print("Pixel precision: " + str(pixel_precision))
print("Pixel accuracy: " + str(pixel_accuracy))
print("Pixel recall: " + str(pixel_sensitivity))
print("Pixel F1-measure: " + str(pixel_F1))
print("Pixel TP: " + str(pixelTP))
print("Pixel FP: " + str(pixelFP))
print("Pixel FN: " + str(pixelFN))
print("Pixel TN: " + str(pixelTN))
if window_method != 'None':
[window_precision, window_sensitivity, window_accuracy, window_F1
] = evalf.performance_evaluation_window(windowTP, windowFN,
windowFP)
print("Window precision: " + str(window_precision))
print("Window accuracy: " + str(window_accuracy))
print("Window F1-measure: " + str(window_F1))
return [
pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity,
pixel_F1, window_precision, window_accuracy, window_F1, counter
]
for ii in range(len(result_files)):
# Read mask file
candidate_masks_name = '{}/{}/{}'.format(results_dir, method,
result_files[ii])
print('File: {}'.format(candidate_masks_name))
pixelCandidates = imageio.imread(candidate_masks_name) > 0
# Accumulate pixel performance of the current image %%%%%%%%%%%%%%%%%
name, ext = os.path.splitext(test_files[ii])
gt_mask_name = '{}/mask/mask.{}.png'.format(test_dir, name)
pixelAnnotation = imageio.imread(gt_mask_name) > 0
[localPixelTP, localPixelFP, localPixelFN, localPixelTN
] = evalf.performance_accumulation_pixel(pixelCandidates,
pixelAnnotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
if window_evaluation == 1:
# Read .pkl file
name_r, ext_r = os.path.splitext(result_files[ii])
pkl_name = '{}/{}/{}.pkl'.format(results_dir, method, name_r)
with open(pkl_name, "rb") as fp: # Unpickling
windowCandidates = pickle.load(fp)
gt_annotations_name = '{}/gt/gt.{}.txt'.format(test_dir, name)
def traffic_sign_detection_test(directory, output_dir, pixel_method, window_method, use_dataset="training"):
"""
Calculates all statistical evaluation metrics of different pixel selector method (TRAINING AND VALIDATION)
* Inputs:
- directory = path to train images
- outpit_dir = Directory where to store output masks, etc. For instance '~/m1-results/week1/test'
- pixel_method = pixel method that will segmentate the image
- window_method = -------
*Outputs:
- pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity, window_precision, window_accuracy
"""
pixelTP = 0
pixelFN = 0
pixelFP = 0
pixelTN = 0
windowTP = 0
windowFN = 0
windowFP = 0
window_precision = 0
window_accuracy = 0
window_sensitivity = 0
# print("splitting in trainning test")
# Load image names in the given directory
# file_names = sorted(fnmatch.filter(os.listdir(directory), '*.jpg'))
signals_type_dict = get_dictionary()
training, validation = [], []
for key in signals_type_dict:
sig_subdict = signals_type_dict[key]
training_type, validation_type = divide_training_validation_SL(sig_subdict['signal_list'])
training.extend(training_type)
validation.extend(validation_type)
# print("extracting mask")
dataset = training
if(use_dataset == 'validation'):
dataset = validation
# if(CONSOLE_ARGUMENTS.use_test):
totalTime = 0
dataset_paths = [signal.img_orig_path for signal in dataset]
for signal_path in tqdm(dataset_paths, ascii=True, desc="Calculating Statistics"):
startTime = time.time()
rgb_mask, bb_list = get_pixel_candidates(signal_path)
totalTime = time.time() - startTime
if(bb_list is not None): bb_list = convertBBFormat(bb_list)
_, name = signal_path.rsplit('/', 1)
base, extension = os.path.splitext(name)
# Accumulate pixel performance of the current image #################
pixel_annotation = imageio.imread('{}/mask/mask.{}.png'.format(directory,base)) > 0
[localPixelTP, localPixelFP, localPixelFN, localPixelTN] =\
evalf.performance_accumulation_pixel(rgb_mask, pixel_annotation)
pixelTP = pixelTP + localPixelTP
pixelFP = pixelFP + localPixelFP
pixelFN = pixelFN + localPixelFN
pixelTN = pixelTN + localPixelTN
[pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity] =\
evalf.performance_evaluation_pixel(pixelTP, pixelFP, pixelFN, pixelTN)
if(bb_list != None):
# Accumulate object performance of the current image ################
window_annotationss = load_annotations('{}/gt/gt.{}.txt'.format(directory, base))
[localWindowTP, localWindowFN, localWindowFP] = \
evalf.performance_accumulation_window(bb_list, window_annotationss)
windowTP = windowTP + localWindowTP
windowFN = windowFN + localWindowFN
windowFP = windowFP + localWindowFP
# Plot performance evaluation
[window_precision, window_sensitivity, window_accuracy] = \
evalf.performance_evaluation_window(windowTP, windowFN, windowFP)
print("meanTime", totalTime/len(dataset))
print("pixelTP", pixelTP, "\t", pixelFP, "\t", pixelFN)
print("windowTP", windowTP, "\t", windowFP, "\t", windowFN)
return [pixel_precision, pixel_accuracy, pixel_specificity, pixel_sensitivity,\
window_precision, window_accuracy, window_sensitivity]