def main():
args = get_args()
config = json.load(open(args.config, "r"))
weights = args.weights
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'], input_size,
config['model']['anchors'])
yolo.load_weights(weights)
threshold = args.threshold
cap = cv2.VideoCapture(0)
video_idx = 0
writing_video = False
writer = None
while True:
ret, frame = cap.read()
frame, vizframe = prep_image(frame, input_size[0])
print(frame.shape)
height, width = frame.shape[:2]
prediction_time, boxes, probs = yolo.predict(frame[np.newaxis, ...],
height, width,
float(threshold))
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
n_copy = draw_boxes(vizframe, boxes, probs, config['model']['labels'])
n_c_v = to_bgr(n_copy)
if writing_video:
writer.write(n_c_v)
cv2.imshow("annotated", n_c_v)
ky = cv2.waitKey(1) & 0xFF
if ky == ord('s'):
if not writing_video:
fcc = cv2.VideoWriter_fourcc(*'VP90') # VP90 MJPG
writer = cv2.VideoWriter(f'output-{video_idx}.avi', fcc, 25.0,
(224, 224))
print(f" START writing output-{video_idx}.avi")
writing_video = True
else:
print(f" STOP writing output-{video_idx}.avi")
writer.release()
writing_video = False
video_idx += 1
if ky == ord('q'):
if writing_video:
writer.release()
break
cap.release()
def train_from_config(config, project_folder):
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
# Create the converter
converter = Converter(config['converter']['type'],
config['model']['architecture'], input_size,
config['train']['valid_image_folder'])
# Detector
if config['model']['type'] == 'Detector':
if config['train']['is_only_detect']:
labels = ["object"]
else:
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_object_labels(
config['train']['train_annot_folder'])
print("labels: ", labels)
print("training on: ", config['train']['train_image_folder'],
"and validating on: ", config['train']['train_annot_folder'])
# 1. Construct the model
yolo = create_yolo(
config['model']['architecture'], labels, input_size,
config['model']['anchors'], config['model']['coord_scale'],
config['model']['class_scale'], config['model']['object_scale'],
config['model']['no_object_scale'], config['weights']['backend'])
# 2. Load the pretrained weights (if any)
yolo.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = yolo.train(
config['train']['train_image_folder'],
config['train']['train_annot_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['valid_annot_folder'],
config['train']['first_trainable_layer'],
config['train']['valid_metric'])
# 4 Convert the model
converter.convert_model(model_path, model_layers,
config['train']['valid_image_folder'])
return model_path
def setup_inference(config,
weights,
threshold=0.3,
path=None,
dataset="testing"):
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
"""make directory to save inference results """
dirname = os.path.join(os.path.dirname(weights), 'Inference_results')
if os.path.isdir(dirname):
print(
"Folder {} is already exists. Image files in directory might be overwritten"
.format(dirname))
else:
print("Folder {} is created.".format(dirname))
os.makedirs(dirname)
if config['model']['type'] == 'Detector':
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'], input_size,
config['model']['anchors'])
yolo.load_weights(weights)
# 3. read image
# 3. read image
if dataset == 'testing':
print("the dataset used for testing is:",
config['test']['test_image_folder'],
" the annotations are: ",
config['test']['test_label_folder'])
# added testing directly in configuration
annotations = parse_annotation(
config['test']['test_label_folder'],
config['test']['test_image_folder'],
config['model']['labels'],
is_only_detect=config['train']['is_only_detect'])
else:
print("the dataset used for testing is:",
config['train']['valid_image_folder'],
" the annotations are: ",
config['train']['valid_annot_folder'])
annotations = parse_annotation(
config['train']['valid_annot_folder'],
config['train']['valid_image_folder'],
config['model']['labels'],
is_only_detect=config['train']['is_only_detect'])
n_true_positives = 0
n_truth = 0
n_pred = 0
inference_time = []
for i in range(len(annotations)):
img_path = annotations.fname(i)
img_fname = os.path.basename(img_path)
true_boxes = annotations.boxes(i)
true_labels = annotations.code_labels(i)
orig_image, input_image = prepare_image(img_path, yolo)
height, width = orig_image.shape[:2]
prediction_time, boxes, probs = yolo.predict(
input_image, height, width, float(threshold))
inference_time.append(prediction_time)
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
orig_image = draw_scaled_boxes(orig_image, boxes, probs,
config['model']['labels'])
output_path = os.path.join(dirname, os.path.split(img_fname)[-1])
cv2.imwrite(output_path, orig_image)
print("{}-boxes are detected. {} saved.".format(
len(boxes), output_path))
show_image(output_path)
n_true_positives += count_true_positives(boxes, true_boxes, labels,
true_labels)
n_truth += len(true_boxes)
n_pred += len(boxes)
print(calc_score(n_true_positives, n_truth, n_pred))
if len(inference_time) > 1:
print("Average prediction time:{} ms".format(
sum(inference_time[1:]) / len(inference_time[1:])))
def train_from_config(config, project_folder):
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
# Create the converter
converter = Converter(config['converter']['type'],
config['model']['architecture'], input_size,
config['train']['valid_image_folder'])
# Segmentation network
if config['model']['type'] == 'SegNet':
print('Segmentation')
# 1. Construct the model
segnet = create_segnet(config['model']['architecture'], input_size,
config['model']['n_classes'],
config['weights']['backend'])
# 2. Load the pretrained weights (if any)
segnet.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = segnet.train(
config['train']['train_image_folder'],
config['train']['train_annot_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['valid_annot_folder'],
config['train']['first_trainable_layer'],
config['train']['ignore_zero_class'],
config['train']['valid_metric'])
# Classifier
if config['model']['type'] == 'Classifier':
print('Classifier')
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_labels(config['train']['train_image_folder'])
# 1. Construct the model
classifier = create_classifier(config['model']['architecture'], labels,
input_size,
config['model']['fully-connected'],
config['model']['dropout'],
config['weights']['backend'],
config['weights']['save_bottleneck'])
# 2. Load the pretrained weights (if any)
classifier.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = classifier.train(
config['train']['train_image_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['first_trainable_layer'],
config['train']['valid_metric'])
# Detector
if config['model']['type'] == 'Detector':
if config['train']['is_only_detect']:
labels = ["object"]
else:
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_object_labels(
config['train']['train_annot_folder'])
print(labels)
# 1. Construct the model
yolo = create_yolo(
config['model']['architecture'], labels, input_size,
config['model']['anchors'], config['model']['coord_scale'],
config['model']['class_scale'], config['model']['object_scale'],
config['model']['no_object_scale'], config['weights']['backend'])
# 2. Load the pretrained weights (if any)
yolo.load_weights(config['weights']['full'], by_name=True)
# 3. actual training
model_layers, model_path = yolo.train(
config['train']['train_image_folder'],
config['train']['train_annot_folder'],
config['train']['actual_epoch'], project_folder,
config["train"]["batch_size"], config["train"]["augumentation"],
config['train']['learning_rate'], config['train']['train_times'],
config['train']['valid_times'],
config['train']['valid_image_folder'],
config['train']['valid_annot_folder'],
config['train']['first_trainable_layer'],
config['train']['valid_metric'])
# 4 Convert the model
converter.convert_model(model_path, model_layers,
config['train']['valid_image_folder'])
return model_path
def setup_inference(config, weights,threshold=0.3, path=None):
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [config['model']['input_size'],config['model']['input_size']]
"""make directory to save inference results """
dirname = os.path.join(os.path.dirname(weights),'Inference_results')
if os.path.isdir(dirname):
print("Folder {} is already exists. Image files in directory might be overwritten".format(dirname))
else:
print("Folder {} is created.".format(dirname))
os.makedirs(dirname)
if config['model']['type']=='SegNet':
print('Segmentation')
# 1. Construct the model
segnet = create_segnet(config['model']['architecture'],
input_size,
config['model']['n_classes'])
# 2. Load the pretrained weights (if any)
segnet.load_weights(weights)
predict_multiple(segnet._network, inp_dir=config['train']['valid_image_folder'], out_dir=dirname, overlay_img=True)
print(evaluate(segnet._network, inp_images_dir=config['train']['valid_image_folder'], annotations_dir=config['train']['valid_annot_folder']))
if config['model']['type']=='Classifier':
print('Classifier')
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_labels(config['train']['train_image_folder'])
# 1.Construct the model
classifier = create_classifier(config['model']['architecture'],
labels,
input_size,
config['model']['fully-connected'],
config['model']['dropout'])
# 2. Load the pretrained weights (if any)
classifier.load_weights(weights)
font = cv2.FONT_HERSHEY_SIMPLEX
valid_image_folder = config['train']['valid_image_folder']
image_files_list = glob.glob(valid_image_folder + '/**/*.jpg', recursive=True)
inference_time = []
for filename in image_files_list:
output_path = os.path.join(dirname, os.path.basename(filename))
orig_image, input_image = prepare_image(filename, classifier)
prediction_time, img_class, prob = classifier.predict(input_image)
inference_time.append(prediction_time)
print(orig_image.shape)
cv2.putText(orig_image, "{}:{:.2f}".format(img_class[0], prob[0]), (10,30), font, orig_image.shape[1]/700 , (0, 0, 255), 2, True)
cv2.imwrite(output_path, orig_image)
show_image(output_path)
print("{}:{}".format(img_class[0], prob[0]))
if len(inference_time)>1:
print("Average prediction time:{} ms".format(sum(inference_time[1:])/len(inference_time[1:])))
if config['model']['type']=='Detector':
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'],
input_size,
config['model']['anchors'])
yolo.load_weights(weights)
# 3. read image
annotations = parse_annotation(config['train']['valid_annot_folder'],
config['train']['valid_image_folder'],
config['model']['labels'],
is_only_detect=config['train']['is_only_detect'])
n_true_positives = 0
n_truth = 0
n_pred = 0
inference_time = []
for i in range(len(annotations)):
img_path = annotations.fname(i)
img_fname = os.path.basename(img_path)
true_boxes = annotations.boxes(i)
true_labels = annotations.code_labels(i)
orig_image, input_image = prepare_image(img_path, yolo)
height, width = orig_image.shape[:2]
prediction_time, boxes, probs = yolo.predict(input_image, height, width, float(threshold))
inference_time.append(prediction_time)
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
orig_image = draw_scaled_boxes(orig_image, boxes, probs, config['model']['labels'])
output_path = os.path.join(dirname, os.path.split(img_fname)[-1])
cv2.imwrite(output_path, orig_image)
print("{}-boxes are detected. {} saved.".format(len(boxes), output_path))
show_image(output_path)
n_true_positives += count_true_positives(boxes, true_boxes, labels, true_labels)
n_truth += len(true_boxes)
n_pred += len(boxes)
print(calc_score(n_true_positives, n_truth, n_pred))
if len(inference_time)>1:
print("Average prediction time:{} ms".format(sum(inference_time[1:])/len(inference_time[1:])))
def setup_evaluation(config, weights, threshold=0.3, path=None):
try:
matplotlib.use('TkAgg')
except:
pass
#added for compatibility with < 0.5.7 versions
try:
input_size = config['model']['input_size'][:]
except:
input_size = [
config['model']['input_size'], config['model']['input_size']
]
"""make directory to save inference results """
dirname = os.path.join(os.path.dirname(weights), 'Evaluation_results')
if os.path.isdir(dirname):
print(
"Folder {} is already exists. Image files in directory might be overwritten"
.format(dirname))
else:
print("Folder {} is created.".format(dirname))
os.makedirs(dirname)
if config['model']['type'] == 'SegNet':
print('Segmentation')
# 1. Construct the model
segnet = create_segnet(config['model']['architecture'], input_size,
config['model']['n_classes'])
# 2. Load the pretrained weights (if any)
segnet.load_weights(weights)
for filename in os.listdir(config['train']['valid_image_folder']):
filepath = os.path.join(config['train']['valid_image_folder'],
filename)
orig_image, input_image = prepare_image(filepath, segnet)
output_path = os.path.join(dirname, os.path.basename(filename))
predict(model=segnet._network,
inp=input_image,
image=orig_image,
out_fname=output_path)
#show_image(output_path)
if config['model']['type'] == 'Classifier':
print('Classifier')
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_labels(config['train']['train_image_folder'])
# 1.Construct the model
classifier = create_classifier(config['model']['architecture'], labels,
input_size,
config['model']['fully-connected'],
config['model']['dropout'])
# 2. Load the pretrained weights (if any)
classifier.load_weights(weights)
font = cv2.FONT_HERSHEY_SIMPLEX
valid_image_folder = config['train']['valid_image_folder']
image_files_list = glob.glob(valid_image_folder + '/**/*.jpg',
recursive=True)
inference_time = []
for filename in image_files_list:
output_path = os.path.join(dirname, os.path.basename(filename))
orig_image, input_image = prepare_image(filename, classifier)
prediction_time, img_class, prob = classifier.predict(input_image)
inference_time.append(prediction_time)
# label shape and colorization
text = "{}:{:.2f}".format(img_class[0], prob[0])
background_color = (70, 120, 70
) # grayish green background for text
text_color = (255, 255, 255) # white text
size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0]
left = 10
top = 30 - size[1]
right = left + size[0]
bottom = top + size[1]
# set up the colored rectangle background for text
cv2.rectangle(orig_image, (left - 1, top - 5),
(right + 1, bottom + 1), background_color, -1)
# set up text
cv2.putText(orig_image, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
0.5, text_color, 1)
cv2.imwrite(output_path, orig_image)
#show_image(output_path)
print("{}:{}".format(img_class[0], prob[0]))
if len(inference_time) > 1:
print("Average prediction time:{} ms".format(
sum(inference_time[1:]) / len(inference_time[1:])))
if config['model']['type'] == 'Detector':
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'], input_size,
config['model']['anchors'])
yolo.load_weights(weights)
# 3. read image
annotations = parse_annotation(
config['train']['valid_annot_folder'],
config['train']['valid_image_folder'],
config['model']['labels'],
is_only_detect=config['train']['is_only_detect'])
n_true_positives = 0
n_truth = 0
n_pred = 0
inference_time = []
for i in range(len(annotations)):
img_path = annotations.fname(i)
img_fname = os.path.basename(img_path)
true_boxes = annotations.boxes(i)
true_labels = annotations.code_labels(i)
orig_image, input_image = prepare_image(img_path, yolo)
height, width = orig_image.shape[:2]
prediction_time, boxes, probs = yolo.predict(
input_image, height, width, float(threshold))
inference_time.append(prediction_time)
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
orig_image = draw_boxes(orig_image, boxes, probs,
config['model']['labels'])
output_path = os.path.join(dirname, os.path.split(img_fname)[-1])
cv2.imwrite(output_path, orig_image)
print("{}-boxes are detected. {} saved.".format(
len(boxes), output_path))
#show_image(output_path)
n_true_positives += count_true_positives(boxes, true_boxes, labels,
true_labels)
n_truth += len(true_boxes)
n_pred += len(boxes)
print(calc_score(n_true_positives, n_truth, n_pred))
if len(inference_time) > 1:
print("Average prediction time:{} ms".format(
sum(inference_time[1:]) / len(inference_time[1:])))
