with open(csv_path, 'w') as csvfile:

wr = csv.writer(csvfile)

for i in range(len(csv_content)):

image_path_array = []

for root, dirs, filenames in sorted(os.walk(base_path dir)):

for f in filenames:

if(f.split(".")[1] == "jpg"):

img_path = base_path dir "/" f

image_path_array.append(img_path)

image_path_array.sort()

boxes = sio.loadmat(base_path dir "/polygons.mat")

# there are 100 of these per folder in the egohands dataset

polygons = boxes["polygons"][0]

# first = polygons[0]

# print(len(first))

pointindex = 0

for first in polygons:

font = cv2.FONT_HERSHEY_SIMPLEX

img_id = image_path_array[pointindex]

img = cv2.imread(img_id)

img_params = {}

img_params["width"] = np.size(img, 1)

img_params["height"] = np.size(img, 0)

head, tail = os.path.split(img_id)

img_params["filename"] = tail

img_params["path"] = os.path.abspath(img_id)

img_params["type"] = "train"

pointindex = 1

boxarray = []

csvholder = []

for pointlist in first:

pst = np.empty((0, 2), int)

max_x = max_y = min_x = min_y = 0

findex = 0

for point in pointlist:

if(len(point) == 2):

x = int(point[0])

y = int(point[1])

if(findex == 0):

min_x = x

min_y = y

findex = 1

max_x = x if (x > max_x) else max_x

min_x = x if (x < min_x) else min_x

max_y = y if (y > max_y) else max_y

min_y = y if (y < min_y) else min_y

# print(index, "====", len(point))

appeno = np.array([[x, y]])

pst = np.append(pst, appeno, axis=0)

cv2.putText(img, ".", (x, y), font, 0.7,

(255, 255, 255), 2, cv2.LINE_AA)

hold = {}

hold['minx'] = min_x

hold['miny'] = min_y

hold['maxx'] = max_x

hold['maxy'] = max_y

if (min_x > 0 and min_y > 0 and max_x > 0 and max_y > 0):

boxarray.append(hold)

labelrow = [tail,

np.size(img, 1), np.size(img, 0), "hand", min_x, min_y, max_x, max_y]

csvholder.append(labelrow)

cv2.polylines(img, [pst], True, (0, 255, 255), 1)

cv2.rectangle(img, (min_x, max_y),

(max_x, min_y), (0, 255, 0), 1)

csv_path = img_id.split(".")[0]

if not os.path.exists(csv_path ".csv"):

cv2.putText(img, "DIR : " dir " - " tail, (20, 50),

cv2.FONT_HERSHEY_SIMPLEX, 0.75, (77, 255, 9), 2)

cv2.imshow('Verifying annotation ', img)

save_csv(csv_path ".csv", csvholder)

print("===== saving csv file for ", tail)

header = ['filename', 'width', 'height',

'class', 'xmin', 'ymin', 'xmax', 'ymax']

for root, dirs, filenames in os.walk(image_dir):

for dir in dirs:

csvholder = []

csvholder.append(header)

loop_index = 0

for f in os.listdir(image_dir dir):

if(f.split(".")[1] == "csv"):

loop_index = 1

#print(loop_index, f)

csv_file = open(image_dir dir "/" f, 'r')

reader = csv.reader(csv_file)

for row in reader:

csvholder.append(row)

csv_file.close()

os.remove(image_dir dir "/" f)

save_csv(image_dir dir "_labels.csv", csvholder)

print("Saved label csv for ", dir, image_dir

create_directory("data")

create_directory("data/train")

create_directory("data/eval")

loop_index = 0

"""

data_size = 4000

data_sampsize = int(0.1 * data_size)

random.seed(1)

test_samp_array = random.sample(range(data_size), k=data_sampsize)

"""

for root, dirs, filenames in os.walk(image_dir):

for dir in dirs:

for f in os.listdir(image_dir dir):

if(f.split(".")[1] == "jpg"):

loop_index = 1

#print('DEBUG: loop_index, f',loop_index, f)

#print('DEBUG: f.split(".")[0]',f.split(".")[0])

#if loop_index in test_samp_array:

if not np.mod(loop_index,10):

os.rename(image_dir dir

"/" f, "data/eval/" f)

os.rename(image_dir dir

"/" f.split(".")[0] ".csv", "data/eval/" f.split(".")[0] ".csv")

else:

os.rename(image_dir dir

"/" f, "data/train/" f)

os.rename(image_dir dir

"/" f.split(".")[0] ".csv", "data/train/" f.split(".")[0] ".csv")

print(loop_index, image_dir f)

print("> done scanning director ", dir)

os.remove(image_dir dir "/polygons.mat")

os.rmdir(image_dir dir)

print("Train/Eval content generation complete!")

for root, dirs, filenames in os.walk(image_dir):

for dir in dirs:

get_bbox_visualize(image_dir, dir)

print("CSV generation complete!\nGenerating train/eval folders")

print("Renaming files")

loop_index = 0

for root, dirs, filenames in sorted(os.walk(image_dir)):

for dir in dirs:

for f in os.listdir(image_dir dir):

if (dir not in f):

if(f.split(".")[1] == "jpg"):

loop_index = 1

old = image_dir dir "/" f

new = image_dir dir "/" dir "_" f

os.rename(old, new)

else:

break

if not os.path.exists("egohands"):

zip_ref = zipfile.ZipFile(dataset_path, 'r')

print("> Extracting Dataset files")

zip_ref.extractall("egohands")

print("> Extraction complete")

zip_ref.close()

print("\nTHIS CODE IS BASED ON VICTOR DIBIAs WORK\

\nSEE HIS REPO:\

\nhttps://github.com/victordibia/handtracking\n")

is_downloaded = os.path.exists(dataset_path)

if not is_downloaded:

print(

"> downloading Egohands dataset (1.3GB)")

opener = urllib.request.URLopener()

opener.retrieve(dataset_url, dataset_path)

print("> download complete")

extract_folder(dataset_path)

else:

print("Egohands dataset already downloaded.\nGenerating CSV files")

label_map = "data/label_map.pbtxt"

if not os.path.isfile(label_map):

f = open(label_map,"w")

f.write("item {\n id: 1\n name: 'hand'\n}")

f.close()

cwd = os.getcwd()

for directory in ['train','eval']:

src_dir = cwd '/data/{}/'.format(directory)

drc_dir = cwd '/data/{}/images/'.format(directory)

create_directory(drc_dir)

for file in os.listdir(src_dir):

if file.endswith(".jpg"):

sh.move(src_dir file,drc_dir file)

sh.rmtree('egohands')

#os.remove(EGO_HANDS_FILE)

print('\n> creating the dataset complete\

\n> you can now start training\

EGOHANDS_DATASET_URL = "http://vision.soic.indiana.edu/egohands_files/egohands_data.zip"

EGO_HANDS_FILE = "egohands_data.zip"

download_egohands_dataset(EGOHANDS_DATASET_URL, EGO_HANDS_FILE)

create_label_map()