def test_train_yolo_framework(setup_model_config, setup_weights_file,
setup_dataset_folder, setup_image_and_its_boxes):
model_config = setup_model_config
pretrained_feature_file, weight_file = setup_weights_file
img_folder, ann_folder = setup_dataset_folder
# 1. Construct the model
yolo = create_yolo(model_config['architecture'], model_config['labels'],
model_config['input_size'], model_config['anchors'],
pretrained_feature_file)
# 2. warmup training
yolo.train(img_folder, ann_folder, 2, weight_file, 2, False, 1e-4, 10, 1,
2, img_folder, ann_folder)
# 3. Load the warmup trained weights
yolo.load_weights(weight_file)
# 4. actual training
yolo.train(img_folder, ann_folder, 12, weight_file, 2, False, 1e-4, 10, 1,
0, img_folder, ann_folder)
# 5. Load training image & predict objects
image, true_boxes = setup_image_and_its_boxes
boxes, probs = yolo.predict(image)
boxes = to_centroid(boxes)
assert len(boxes) == 2
assert len(probs) == 2
assert np.allclose(np.argmax(probs, axis=1), [0, 3])
for box, true_box in zip(boxes, true_boxes):
iou = centroid_box_iou(box, true_box)
assert iou > 0.4
def initYolo(): # laddar in modellen och Konfigurerar yolo.
with open(DEFAULT_CONFIG_FILE) as config_buffer:
config = json.loads(config_buffer.read())
if config['train']['is_only_detect']:
labels = ['']
else:
if config['model']['labels']:
labels = config['model']['labels']
else:
labels = get_object_labels(config['train']['train_annot_folder'])
print(labels)
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
labels,
config['model']['input_size'],
config['model']['anchors'])
yolo.load_weights(DEFAULT_WEIGHT_FILE)
return yolo,labels
def test_predict(setup_image_and_its_boxes, setup_outputs, setup_config):
# 1. Given
image, true_boxes = setup_image_and_its_boxes
model_config = setup_config
desired_boxes, desired_probs = setup_outputs
# 2. When run
# 1. Construct the model
yolo = create_yolo(model_config['architecture'], model_config['labels'],
model_config['input_size'], model_config['anchors'])
yolo.load_weights(os.path.join(TEST_SAMPLE_DIR, "mobile_288_weights.h5"))
boxes, probs = yolo.predict(image)
boxes = to_centroid(boxes)
assert len(boxes) == 2
assert len(probs) == 2
for box, true_box in zip(boxes, true_boxes):
iou = centroid_box_iou(box, true_box)
assert iou > 0.5
'--weights',
default=DEFAULT_WEIGHT_FILE,
help='trained weight files')
if __name__ == '__main__':
# 1. extract arguments
args = argparser.parse_args()
with open(args.conf) as config_buffer:
config = json.loads(config_buffer.read())
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'],
config['model']['input_size'],
config['model']['anchors'])
yolo.load_weights(args.weights)
# 3. read image
write_dname = "detected"
if not os.path.exists(write_dname): os.makedirs(write_dname)
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
for i in range(len(annotations)):
img_path = annotations.fname(i)
from yolo.backend.utils.box import draw_scaled_boxes
import spotipy.util as util
DEFAULT_CONFIG_FILE = "./image_det/config.json"
DEFAULT_WEIGHT_FILE = "./image_det/model.h5"
config_buffer = open(DEFAULT_CONFIG_FILE, 'r')
config = json.loads(config_buffer.read())
playlist = []
postion = []
host_name_list = []
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'], config['model']['labels'],
config['model']['input_size'], config['model']['anchors'])
yolo.load_weights(DEFAULT_WEIGHT_FILE)
hostname = socket.gethostname()
def Connect_to_token(): #ansluter till spotify token
global spotifyObject_1, host_name_list
#kollar om programmet har använts på användarens dator förut
for i in range(0, len(config["spotify_config"]["Devices"])):
host_name_list.append(
config["spotify_config"]["Devices"][i]["devuce-name"])
if hostname in host_name_list:
config["spotify_config"]["USERNAME"] = config["spotify_config"][
"Devices"][host_name_list.index(hostname)]["USERNAME"]
else:
from jetcam.utils import bgr8_to_jpeg
from jetcam.csi_camera import CSICamera
import argparse
import json
import cv2
import numpy as np
from yolo.frontend import create_yolo
from yolo.backend.utils.box import draw_scaled_boxes
from yolo.backend.utils.annotation import parse_annotation
from yolo.backend.utils.eval.fscore import count_true_positives, calc_score
from pascal_voc_writer import Writer
from shutil import copyfile
import os
import yolo
import time
DEFAULT_CONFIG_FILE = os.path.join(yolo.PROJECT_ROOT, "svhn", "config.json")
DEFAULT_WEIGHT_FILE = os.path.join(yolo.PROJECT_ROOT, "svhn", "weights.h5")
DEFAULT_THRESHOLD = 0.3
with open('turn4/config.json') as config_buffer:
config = json.loads(config_buffer.read())
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'], config['model']['labels'],
config['model']['input_size'], config['model']['anchors'])
yolo.load_weights('model.h5')
affinity='euclidean',
linkage='ward')
DEFAULT_CONFIG_FILE = os.path.join(yolo.PROJECT_ROOT, "svhn", "config.json")
DEFAULT_WEIGHT_FILE = os.path.join(yolo.PROJECT_ROOT, "svhn", "weights.h5")
DEFAULT_THRESHOLD = 0.3
threshold = DEFAULT_THRESHOLD
config_file = DEFAULT_CONFIG_FILE
weights_file = DEFAULT_WEIGHT_FILE
with open(config_file) as config_buffer:
config = json.loads(config_buffer.read())
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'], config['model']['labels'],
config['model']['input_size'], config['model']['anchors'])
yolo.load_weights(weights_file)
def get_digits_boxes(img, show_img=False, save_img=False):
img_path = img_path.replace('\\', '/')
# img_path = img_path.replace('\','/')
image = cv2.imread(img_path)
im_size = np.shape(image)
boxes, probs = yolo.predict(image, float(threshold))
labels = np.argmax(probs, axis=1) if len(probs) > 0 else []
probs = np.max(probs, axis=1) if len(probs) > 0 else []
# 4. save detection result
image = draw_scaled_boxes(image, boxes, probs, config['model']['labels'])
centers = []
'--weights',
default=DEFAULT_WEIGHT_FILE,
help='trained weight files')
if __name__ == '__main__':
# 1. extract arguments
args = argparser.parse_args()
with open(args.conf) as config_buffer:
config = json.loads(config_buffer.read())
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'],
config['model']['input_size'],
config['model']['anchors'])
yolo.load_weights(args.weights)
# 3. read image
write_dname = "detected"
if not os.path.exists(write_dname): os.makedirs(write_dname)
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
for i in range(len(annotations)):
img_path = annotations.fname(i)
img_fname = os.path.basename(img_path)
os.path.join('datasets', evaluation_object, 'images_test', filename),
224, 224)
writer.addObject(label_list[labels[0]], boxes[0][0], boxes[0][1],
boxes[0][2], boxes[0][3])
name = filename.split('.')
writer.save('evaluation/ann/' + name[0] + '.xml')
if __name__ == "__main__":
# 2. create yolo instance & predict
yolo = create_yolo(config['model']['architecture'],
config['model']['labels'],
config['model']['input_size'],
config['model']['anchors'])
# yolo.load_weights(DEFAULT_WEIGHT_FILE)
yolo.load_weights("./weights.h5")
# 3. read image
write_dname = "evaluation/detected"
if not os.path.exists(write_dname): os.makedirs(write_dname)
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
# for i in range(len(annotations)):
for filename in os.listdir('datasets/' + evaluation_object +
'/images_test'):
