def main(args):
global face_detector
global classifier_object
if (not args.checkpoint_path):
raise ValueError(
'You must supply the checkpoint path with --checkpoint_path')
if (not os.path.exists(args.checkpoint_path)):
print(
'The checkpoint path is missing. Error processing the data source without the checkpoint path.'
)
return (False)
if (not args.dataset_dir):
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if (not os.path.exists(args.dataset_dir)):
print(
'The dataset directory is missing. Error processing the data source without the dataset directory.'
)
return (False)
if (args.model_root_dir):
model_root_dir = args.model_root_dir
else:
model_root_dir = NetworkFactory.model_deploy_dir()
last_network = 'ONet'
face_detector = FaceDetector(last_network, model_root_dir)
classifier_object = Classifier()
if (not classifier_object.load_dataset(args.dataset_dir)):
return (False)
if (not classifier_object.load_model(args.checkpoint_path, args.model_name,
args.gpu_memory_fraction)):
return (False)
image1 = cv2.imread(args.image1)
input_image_height, input_image_width, input_image_channels = image1.shape
#print(input_image_height, input_image_width)
image2 = cv2.imread(args.image2)
input_image_height, input_image_width, input_image_channels = image2.shape
#print(input_image_height, input_image_width)
i1 = features(image1)
i2 = features(image2)
#if(not (i1 and i2)):
#return(False)
result = l1_loss(i1, i2)
print("The answer is " + str(result))
result = l2_loss(i1, i2)
print("The answer is " + str(result))
result = dot_product(i1, i2)
print("The answer is " + str(result))
def main(args):
global classifier_object
if(not args.checkpoint_path):
raise ValueError('You must supply the checkpoint path with --checkpoint_path')
if(not os.path.exists(args.checkpoint_path)):
print('The checkpoint path is missing. Error processing the data source without the checkpoint path.')
return(False)
if(not args.dataset_dir):
raise ValueError('You must supply the dataset directory with --dataset_dir')
if(not os.path.exists(args.dataset_dir)):
print('The dataset directory is missing. Error processing the data source without the dataset directory.')
return(False)
classifier_object = Classifier()
if(not classifier_object.load_dataset(args.dataset_dir)):
return(False)
if(not classifier_object.load_model(args.checkpoint_path, args.model_name, args.gpu_memory_fraction)):
return(False)
directory = args.directory
onlyfiles=[f for f in listdir(directory) if isfile (join(directory,f))]
images = np.empty(len(onlyfiles),dtype=object)
print('Processing ', len(onlyfiles), ' number of images.')
for n in range(0,len(onlyfiles)):
image = cv2.imread(join(directory, onlyfiles[n]))
images[n] = image
min_l1=1
max_l1=0
min_l2=1
max_l2=0
min_dot=1
max_dot=0
for i in range(0,len(images)):
input_image_height, input_image_width, input_image_channels = images[i].shape
#print(input_image_height, input_image_width)
for j in range(i,len(images)):
input_image_height, input_image_width, input_image_channels = images[j].shape
#print(input_image_height, input_image_width)
i1 = features(images[i])
i2 = features(images[j])
result=l1_loss(i1,i2)
if(result<min_l1):
min_l1=result
#l1_i1=images[i]
#l1_i2=images[j]
if(result>max_l1):
max_l1=result
#l1_i1=images[i]
#l1_i2=images[j]
result=l2_loss(i1,i2)
if(result<min_l2):
min_l2=result
if(result>max_l2):
max_l2=result
result=dot_product(i1,i2)
if(result < 0.4):
print('Non simmilar files (', result,') are - ', onlyfiles[i], onlyfiles[j])
if(result<min_dot):
min_dot=result
if(result>max_dot):
max_dot=result
print ("L1 loss minimum value is",min_l1)
print ("L1 loss maximum value is",max_l1)
print ("L2 loss minimum value is",min_l2)
print ("L2 loss maximum value is",max_l2)
print ("arcosine loss minimum value is",min_dot)
print ("arcosine loss maximum value is",max_dot)
def main(args):
probability_threshold = 50.0
if (not args.input_tsv_file):
raise ValueError(
'You must supply input TSV file with --input_tsv_file.')
if (not args.output_tsv_file):
raise ValueError(
'You must supply output TSV file with --output_tsv_file.')
if (not os.path.isfile(args.input_tsv_file)):
return (False)
model_root_dir = NetworkFactory.model_deploy_dir()
last_network = 'ONet'
face_detector = FaceDetector(last_network, model_root_dir)
classifier_object = Classifier()
if (not classifier_object.load_dataset(args.dataset_dir)):
return (False)
if (not classifier_object.load_model(args.checkpoint_path, args.model_name,
args.gpu_memory_fraction)):
return (False)
network_size = classifier_object.network_image_size()
number_of_images = 0
good_images = 0
input_tsv_file = open(args.input_tsv_file, 'r')
output_tsv_file = open(args.output_tsv_file, 'w')
while (True):
input_data = input_tsv_file.readline().strip()
if (not input_data):
break
number_of_images += 1
fields = input_data.split('\t')
line_number = str(fields[0])
image_string = fields[1]
decoded_image_string = base64.b64decode(image_string)
image_data = np.fromstring(decoded_image_string, dtype=np.uint8)
input_image = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
height, width, channels = input_image.shape
cv2.imwrite('image.png', input_image)
#misc.imsave('image.png', input_image)
input_image = misc.imread('image.png')
input_clone = np.copy(input_image)
boxes_c, landmarks = face_detector.detect(input_clone)
face_probability = 0.0
found = False
crop_box = []
for index in range(boxes_c.shape[0]):
if (boxes_c[index, 4] > face_probability):
found = True
face_probability = boxes_c[index, 4]
bounding_box = boxes_c[index, :4]
crop_box = [
int(max(bounding_box[0], 0)),
int(max(bounding_box[1], 0)),
int(min(bounding_box[2], width)),
int(min(bounding_box[3], height))
]
if (found):
cropped_image = input_image[crop_box[1]:crop_box[3],
crop_box[0]:crop_box[2], :]
else:
cropped_image = input_image
#resized_image = cv2.resize(cropped_image, (network_size, network_size), interpolation=cv2.INTER_LINEAR)
resized_image = misc.imresize(cropped_image,
(network_size, network_size),
interp='bilinear')
class_names_probabilities = classifier_object.classify(
resized_image, print_results=False)
predicted_name = ''
probability = 0.0
if (len(class_names_probabilities) > 0):
names = map(operator.itemgetter(0), class_names_probabilities)
probabilities = map(operator.itemgetter(1),
class_names_probabilities)
predicted_name = str(names[0])
probability = probabilities[0]
if ((probability > probability_threshold)
or (probability >
(probabilities[1] + probabilities[2] / 2.0))):
good_images += 1
print(number_of_images, ', predicted_name -', predicted_name,
', probability -', probability)
print('Accuracy = ', (good_images * 100.0) / number_of_images, ' for ',
number_of_images, ' images.')
#cv2.imshow('image', cropped_image)
#cv2.waitKey();
output_tsv_file.write(line_number + '\t' + str(predicted_name) + '\t' +
str(probability) + '\n')
print('Accuracy = ', (good_images * 100.0) / number_of_images, ' for ',
number_of_images, ' images.')
return (True)
def process(args):
class_names = DatasetAnalyzer.read_class_names(args.class_name_file)
if(len(class_names) == 0):
class_names = DatasetAnalyzer.get_class_names(args.source_dir)
no_of_classes = len(class_names)
if(no_of_classes == 0):
return(False)
classifier_object = Classifier()
if(not classifier_object.load_dataset(args.dataset_dir)):
return(False)
if(not classifier_object.load_model(args.checkpoint_path, args.model_name, args.gpu_memory_fraction)):
return(False)
source_path = os.path.expanduser(args.source_dir)
target_path = os.path.expanduser(args.target_dir)
good_files = 0
bad_files = 0
processed_classes = 0
show_result = int(max( (no_of_classes * (5.0/100.0)), 10) )
for class_name in class_names:
source_class_dir = os.path.join(source_path, class_name)
if(not os.path.isdir(source_class_dir)):
continue
image_file_names = os.listdir(source_class_dir)
for image_file_name in image_file_names:
source_filename = os.path.join(source_class_dir, image_file_name)
if(not os.path.isfile(source_filename)):
continue
try:
current_image = cv2.imread(source_filename, cv2.IMREAD_COLOR)
except (IOError, ValueError, IndexError) as error:
continue
if(current_image is None):
continue
class_names_probabilities = classifier_object.classify(current_image, args.use_top)
is_good = False
for predicted_name, probability in class_names_probabilities:
if(predicted_name == class_name):
is_good = True
break
if(is_good):
good_files = good_files + 1
else:
target_class_dir = os.path.join(target_path, class_name)
if( not os.path.exists(target_class_dir) ):
os.makedirs(target_class_dir)
target_filename = os.path.join(target_class_dir, image_file_name)
os.rename(source_filename, target_filename)
bad_files = bad_files + 1
processed_classes += 1
if( processed_classes % show_result == 0):
print(processed_classes, ' classes are processed.')
print("Good files are - " + str(good_files) + " and bad files are - " + str(bad_files))
return(True)
def main(args):
if (not args.checkpoint_path):
raise ValueError(
'You must supply the checkpoint path with --checkpoint_path')
if (not os.path.exists(args.checkpoint_path)):
print(
'The checkpoint path is missing. Error processing the data source without the checkpoint path.'
)
return (False)
if (not args.dataset_dir):
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
if (not os.path.exists(args.dataset_dir)):
print(
'The dataset directory is missing. Error processing the data source without the dataset directory.'
)
return (False)
if (args.model_root_dir):
model_root_dir = args.model_root_dir
else:
model_root_dir = NetworkFactory.model_deploy_dir()
last_network = 'ONet'
face_detector = FaceDetector(last_network, model_root_dir)
classifier_object = Classifier()
if (not classifier_object.load_dataset(args.dataset_dir)):
return (False)
if (not classifier_object.load_model(args.checkpoint_path, args.model_name,
args.gpu_memory_fraction)):
return (False)
webcamera = cv2.VideoCapture(args.webcamera_id)
webcamera.set(3, 600)
webcamera.set(4, 800)
image = cv2.imread('/git-space/16.jpg')
input_image_height, input_image_width, input_image_channels = image.shape
print(input_image_height, input_image_width)
face_probability = 0.75
minimum_face_size = 24
while True:
start_time = cv2.getTickCount()
status, current_frame = webcamera.read()
is_busy = False
if status:
current_image = np.array(current_frame)
image_clone = np.copy(current_image)
if (is_busy):
continue
is_busy = True
boxes_c, landmarks = face_detector.detect(image_clone)
end_time = cv2.getTickCount()
time_duration = (end_time - start_time) / cv2.getTickFrequency()
frames_per_sec = 1.0 / time_duration
cv2.putText(current_frame, '{:.2f} FPS'.format(frames_per_sec),
(10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0),
2)
for index in range(boxes_c.shape[0]):
bounding_box = boxes_c[index, :4]
probability = boxes_c[index, 4]
crop_box = []
if (probability > face_probability):
height, width, channels = image_clone.shape
crop_box = [
int(max(bounding_box[0], 0)),
int(max(bounding_box[1], 0)),
int(min(bounding_box[2], width)),
int(min(bounding_box[3], height))
]
cropped_image = image_clone[crop_box[1]:crop_box[3],
crop_box[0]:crop_box[2], :]
crop_height, crop_width, crop_channels = cropped_image.shape
if (crop_height < minimum_face_size) or (
crop_width < minimum_face_size):
continue
cv2.rectangle(image_clone, (crop_box[0], crop_box[1]),
(crop_box[2], crop_box[3]), (0, 255, 0), 1)
class_names_probabilities = classifier_object.classify(
cropped_image, 1)
predicted_name = class_names_probabilities[0][0]
probability = class_names_probabilities[0][1]
if (probability > args.threshold):
cv2.putText(
image_clone,
predicted_name + ' - {:.2f}'.format(probability),
(crop_box[0], crop_box[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow("", image_clone)
is_busy = False
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
print('Error detecting the webcamera.')
break
webcamera.release()
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if(not os.path.exists(output_dir)):
os.mkdir(output_dir)
is_processed = {}
probability_threshold = [ 0.95, 0.90, 0.85, 0.80 ]
if(not args.input_tsv_file):
raise ValueError('You must supply input TSV file with --input_tsv_file.')
if(not os.path.isfile(args.input_tsv_file)):
return(False)
model_root_dir = NetworkFactory.model_deploy_dir()
last_network='ONet'
face_detector = FaceDetector(last_network, model_root_dir)
classifier_object = Classifier()
if(not classifier_object.load_dataset(args.dataset_dir)):
return(False)
if(not classifier_object.load_model(args.checkpoint_path, args.model_name, args.gpu_memory_fraction)):
return(False)
network_size = classifier_object.network_image_size()
celebrity_count = 0
for current_threshold in probability_threshold:
input_tsv_file = open(args.input_tsv_file, 'r')
while( True ):
input_data = input_tsv_file.readline().strip()
if( not input_data ):
break
fields = input_data.split('\t')
class_name = str(fields[2])
if class_name in is_processed.keys():
continue
image_string = fields[1]
image_search_rank = fields[3]
decoded_image_string = base64.b64decode(image_string)
image_data = np.fromstring(decoded_image_string, dtype=np.uint8)
input_image = cv2.imdecode(image_data, cv2.IMREAD_COLOR)
height, width, channels = input_image.shape
class_dir = fields[2]
img_name = class_dir + '.png'
cv2.imwrite('image.png', input_image)
#misc.imsave('image.png', input_image)
input_image = misc.imread('image.png')
input_clone = np.copy(input_image)
boxes_c, landmarks = face_detector.detect(input_clone)
face_probability = 0.0
found = False
crop_box = []
for index in range(boxes_c.shape[0]):
if(boxes_c[index, 4] > face_probability):
found = True
face_probability = boxes_c[index, 4]
bounding_box = boxes_c[index, :4]
crop_box = [int(max(bounding_box[0],0)), int(max(bounding_box[1],0)), int(min(bounding_box[2], width)), int(min(bounding_box[3], height))]
if(found):
cropped_image = input_image[crop_box[1]:crop_box[3],crop_box[0]:crop_box[2],:]
else:
cropped_image = input_image
#resized_image = cv2.resize(cropped_image, (network_size, network_size), interpolation=cv2.INTER_LINEAR)
resized_image = misc.imresize(cropped_image, (network_size, network_size), interp='bilinear')
class_names_probabilities = classifier_object.classify(resized_image, print_results=False)
predicted_name = ''
probability = 0.0
if(len(class_names_probabilities) > 0):
names = map(operator.itemgetter(0), class_names_probabilities)
probabilities = map(operator.itemgetter(1), class_names_probabilities)
predicted_name = str(names[0])
probability = probabilities[0]
if( class_name != predicted_name ):
continue
if(probability < current_threshold):
continue
is_processed[class_name] = True
full_class_dir = os.path.join(output_dir, class_dir)
if not os.path.exists(full_class_dir):
os.mkdir(full_class_dir)
celebrity_count = celebrity_count + 1
full_path = os.path.join(full_class_dir, img_name)
cv2.imwrite(full_path, resized_image)
#cv2.imshow('image', cropped_image)
#cv2.waitKey();
print('Processed ', celebrity_count, 'celebrities.')
return(True)