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get_box.py
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get_box.py
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import pathlib
from get_coordinates import get_coordinates
from PIL import Image
import pandas as pd
from show_image import show_image_objects
import os
from kerasretinanet.keras_retinanet import models
from kerasretinanet.keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from kerasretinanet.keras_retinanet.utils.visualization import draw_box, draw_caption
from kerasretinanet.keras_retinanet.utils.colors import label_color
import cv2
import matplotlib.pyplot as plt
import numpy as np
#some fixes so we can train model
import tensorflow.compat.v1 as tf1
config = tf1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf1.InteractiveSession(config=config)
#prepare test pictures and annotations
pic_list = [p for p in pathlib.Path('AWEForSegmentation/testannot_rect').iterdir() if p.is_file()]
dataset = dict()
dataset['img_name'] = list()
dataset['x_min'] = list()
dataset['y_min'] = list()
dataset['x_max'] = list()
dataset['y_max'] = list()
dataset['class_name'] = list()
counter = 0
for name in pic_list:
img_name = str(name)[-8:]
coordinates, object_n = get_coordinates(str(str(name)[-8:-4]), 'testannot_rect')
img_dir = 'AWEForSegmentation/test/' + str(name)[-8:-4] + '.png'
for j in range(object_n):
dataset['x_min'].append(coordinates[j][0])
dataset['y_min'].append(coordinates[j][1])
dataset['x_max'].append(coordinates[j][2])
dataset['y_max'].append(coordinates[j][3])
dataset['img_name'].append(f'test/ears_test_{counter}.jpeg')
dataset['class_name'].append('ear')
img = Image.open(img_dir).convert('RGB')
img.save(f'test/ears_test_{counter}.jpeg', 'JPEG')
counter += 1
df_test = pd.DataFrame(dataset)
df_test.to_csv('df_test.csv')
#prepare train pictures and annotations
pic_list_train = [p for p in pathlib.Path('AWEForSegmentation/trainannot_rect').iterdir() if p.is_file()]
dataset_train = dict()
dataset_train['img_name'] = list()
dataset_train['x_min'] = list()
dataset_train['y_min'] = list()
dataset_train['x_max'] = list()
dataset_train['y_max'] = list()
dataset_train['class_name'] = list()
counter_train = 0
for name in pic_list_train:
img_name = str(name)[-8:]
coordinates, object_n = get_coordinates(str(str(name)[-8:-4]), 'trainannot_rect')
img_dir = 'AWEForSegmentation/train/' + str(name)[-8:-4] + '.png'
for j in range(object_n):
dataset_train['x_min'].append(coordinates[j][0])
dataset_train['y_min'].append(coordinates[j][1])
dataset_train['x_max'].append(coordinates[j][2])
dataset_train['y_max'].append(coordinates[j][3])
dataset_train['img_name'].append(f'train/ears_train_{counter_train}.jpeg')
dataset_train['class_name'].append('ear')
img = Image.open(img_dir).convert('RGB')
img.save(f'train/ears_train_{counter}.jpeg', 'JPEG')
counter_train += 1
df_train = pd.DataFrame(dataset_train)
ANNOTATIONS_FILE = 'annotations.csv'
CLASSES_FILE = 'classes.csv'
df_train.to_csv(ANNOTATIONS_FILE, index = False, header = None)
df_test.to_csv('annotations_test.csv', index = False, header = None)
classes = set(['ear'])
with open(CLASSES_FILE, 'w') as f:
for i, line in enumerate(sorted(classes)):
f.write('{}, {}\n'.format(line, i))