def worker(x):
image_file, output_dir, pixel_method, window_method = x
name = os.path.splitext(os.path.split(image_file)[1])[0]
im = imageio.imread(image_file)
print(image_file)
pixel_candidates = candidate_generation_pixel(im, pixel_method)
window_candidates = candidate_generation_window(im, pixel_candidates,
window_method)
fd = os.path.join(output_dir, '{}_{}'.format(pixel_method, window_method))
if not os.path.exists(fd):
os.makedirs(fd)
out_mask_name = '{}/{}.png'.format(fd, name)
imageio.imwrite(out_mask_name, pixel_candidates.astype(np.uint8))
out_list_name = '{}/{}.pkl'.format(fd, name)
with open(out_list_name, "wb") as fp:
pickle.dump(window_candidates, 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
]