from learning.evaluators import RecallEvaluator as Evaluator
from learning.utils import get_boxes, cal_recall
from utils.visualization import draw_pred_boxes
import cv2
import glob
""" 1. Load dataset """
root_dir = os.path.join('data/face')
test_dir = os.path.join(root_dir, 'test')
IM_SIZE = (512, 512)
NUM_CLASSES = 1
# Load test set
X_test, y_test = dataset.read_data(test_dir, IM_SIZE)
test_set = dataset.DataSet(X_test, y_test)
""" 2. Set test hyperparameters """
class_map = dataset.load_json(os.path.join(test_dir, 'classes.json'))
nms_flag = True
hp_d = dict()
hp_d['batch_size'] = 16
hp_d['nms_flag'] = nms_flag
""" 3. Build graph, load weights, initialize a session and start test """
# Initialize
graph = tf.get_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
model = ConvNet([IM_SIZE[0], IM_SIZE[1], 3], NUM_CLASSES)
evaluator = Evaluator()
saver = tf.train.Saver()
sess = tf.Session(graph=graph, config=config)
import os
import numpy as np
import tensorflow as tf
from datasets import data as dataset
from models.nn import YOLO as ConvNet
from learning.optimizers import MomentumOptimizer as Optimizer
# from learning.optimizers import AdamOptimizer as Optimizer
from learning.evaluators import RecallEvaluator as Evaluator
""" 1. Load and split datasets """
root_dir = os.path.join('.\\data\\face\\') # FIXME
trainval_dir = os.path.join(root_dir, 'train')
# Load anchors
anchors = dataset.load_json(os.path.join(trainval_dir, 'anchors.json'))
# Set image size and number of class
IM_SIZE = (416, 416)
NUM_CLASSES = 1
# Load trainval set and split into train/val sets
X_trainval, y_trainval = dataset.read_data(trainval_dir, IM_SIZE)
trainval_size = X_trainval.shape[0]
val_size = int(trainval_size * 0.1) # FIXME
val_set = dataset.DataSet(X_trainval[:val_size], y_trainval[:val_size])
train_set = dataset.DataSet(X_trainval[val_size:], y_trainval[val_size:])
""" 2. Set training hyperparameters"""
hp_d = dict()
# FIXME: Training hyperparameters
hp_d['batch_size'] = 2
hp_d['num_epochs'] = 50
import os
import numpy as np
import tensorflow as tf
from datasets import data as dataset
from models.nn import YOLO as ConvNet
from learning.optimizers import MomentumOptimizer as Optimizer
# from learning.optimizers import AdamOptimizer as Optimizer
from learning.evaluators import RecallEvaluator as Evaluator
""" 1. Load and split datasets """
root_dir = os.path.join('/home/intern/data/coco') # FIXME
trainval_dir = os.path.join(root_dir, 'images')
# Load anchors
#anchors = dataset.load_json(os.path.join(trainval_dir, 'anchors.json'))
anchors = dataset.load_json(
os.path.join('/home/intern/data/face/train', 'anchors.json'))
# Set image size and number of class
IM_SIZE = (416, 416)
NUM_CLASSES = 1
# Load trainval set and split into train/val sets
X_trainval, y_trainval = dataset.read_data(trainval_dir, IM_SIZE)
trainval_size = X_trainval.shape[0]
val_size = int(trainval_size * 0.1) # FIXME
val_set = dataset.DataSet(X_trainval[:val_size], y_trainval[:val_size])
train_set = dataset.DataSet(X_trainval[val_size:], y_trainval[val_size:])
""" 2. Set training hyperparameters"""
hp_d = dict()
# FIXME: Training hyperparameters