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simple_fxn.py
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import cv2, os, toml, shutil, json,glob, random
from shutil import copyfile
import pandas as pd
def show_annotations(input_folder):
all_files = os.listdir(input_folder)
for cimg in all_files:
if cimg.endswith('.jpg'):
cimg_path = os.path.join(input_folder,cimg)
cannt_path = os.path.join(input_folder,cimg.replace('.jpg','.txt'))
img_file__ = cv2.imread(cimg_path);
annt_file = np.loadtxt(cannt_path)
img_h, img_w, _ = img_file__.shape
img_w, img_h
dims = annt_file.ndim
if dims > 1:
for cbox in annt_file:
cbox = cbox[1:]
x11, y11 = int((cbox[0] - cbox[2]/2)*img_w), int((cbox[1] - cbox[3]/2)*img_h)
x22, y22 = int((cbox[0] + cbox[2]/2)*img_w), int((cbox[1] + cbox[3]/2)*img_h)
img_file__ = cv2.rectangle(img_file__, (x11, y11), (x22, y22), (0,255,0), 3)
else:
cbox = annt_file[1:]
x11, y11 = int((cbox[0] - cbox[2]/2)*img_w), int((cbox[1] - cbox[3]/2)*img_h)
x22, y22 = int((cbox[0] + cbox[2]/2)*img_w), int((cbox[1] + cbox[3]/2)*img_h)
img_file__ = cv2.rectangle(img_file__, (x11, y11), (x22, y22), (0,255,0), 3)
cv2.imshow('',img_file__)
cv2.waitKey(0)
def toml2cvac(automl_path,dst, cls):
if not os.path.isdir(dst):
os.makedirs(os.path.join(dst,'images'))
else:
shutil.rmtree(dst)
cdst = os.path.join(dst,'images')
os.makedirs(cdst)
full_path = os.path.join(automl_path, 'annotations')
files = os.listdir(full_path)
annotations = []
images = []
segmentation = []
image_id = 0; det_id = 0; iscrowd = 0;
categories = []
for cl in cls:
cls_id = cls.index(cl)+1
categories.append({"supercategory": "", "id":cls_id, "name":cl})
img_added = []
img_added_ = []
for curfile in files:
img_added_.append(curfile)
# img_added.append(curfile) # delete
cfilepath = os.path.join(full_path, curfile)
data_configs = toml.load(cfilepath)
for data_config in data_configs['objects']:
w = (data_config['xmax'] - data_config['xmin'])
h = (data_config['ymax'] - data_config['ymin'])
area = w*h
bbox = [data_config['xmin'], data_config['ymin'],
w, h]
category_id = cls.index(data_config['class']) + 1
cobj = {'segmentation': segmentation, 'category_id': category_id, 'id': det_id, 'area': area,
'iscrowd': iscrowd, 'bbox': bbox, 'image_id': image_id}
annotations.append(cobj)
if not curfile in img_added:
cimgs = {"flickr_url": "", "id": image_id, "date_captured": 0, "width": data_configs['width'],
"license": 0, "file_name": curfile.replace('.toml',''),
"coco_url": "", "height": data_configs['height']}
images.append(cimgs)
img_added.append(curfile)
det_id += 1
image_id+=1
df_json = {}
df_json['info'] = {"contributor": "", "year": "", "description": "", "version": "", "url": "", "date_created": ""}
df_json['annotations'] = annotations
df_json['images'] = images
df_json['categories'] = categories
df_json['licenses'] = [{"url": "", "id": 0, "name": ""}]
with open(dst + '/demo.json', 'w') as fp:
json.dump(df_json, fp)
# ## copy images
full_path = os.path.join(automl_path, 'images')
img_files = os.listdir(full_path)
# ccdst = os.path.abspath(dst)
for img in img_files:
src_file = os.path.join(full_path,img)
dst_file = os.path.join(dst, 'images',img)
if os.path.basename(dst_file).replace('.jpg','.toml') in img_added_:
if not(os.path.isfile(dst_file)):
shutil.copy(src_file,dst_file)
def cvat2yolo(cfolder,json_file,img_path, output_path, val_path):
# print (cfolder)
if not os.path.isdir(output_path):
os.makedirs(output_path)
os.makedirs(val_path)
with open(json_file) as f:
json_data = json.load(f)
img_data = json_data['images']
all_annts = json_data['annotations']
# print (json_data['categories'])
cls_names = {}
for catv in json_data['categories']:
cls_names[catv['id']] = catv['name']
print (cls_names)
all_cls = []
cls_indx = list(range(0,len(cls_names)))
cur_cls = list(cls_names.values())
print ('total number of classes: {}'.format(len(cur_cls)))
# val_data = random.sample(img_data,int(len(img_data)*0.2))
# train_data = list(set(img_data).difference(val_data))
random.shuffle(img_data)
val_data = img_data[0:int(len(img_data)*0.2)]
train_data = img_data[int(len(img_data)*0.2):]
# print (cur_cls)
for img in train_data:
cur_img, cur_id, width, height = img['file_name'],img['id'], img['width'], img['height']
annt_match = []
for cur_annt in all_annts:
if(cur_annt['image_id']) == cur_id:
annt_match.append(cur_annt)
full_img_path = os.path.join(img_path,cur_img)
shutil.copy(full_img_path,os.path.join(output_path,cfolder + '_' + cur_img))
frame = cv2.imread(full_img_path)
for cur_match in annt_match:
bbox = cur_match['bbox']
cls = cls_names[cur_match['category_id']]
if not cls in all_cls:all_cls.append(cls)
x1,y1 = int(bbox[0]),int(bbox[1])
x2, y2 = int(bbox[0])+int(bbox[2]),int(bbox[1]+int(bbox[3]))
dw = 1. / width
dh = 1. / height
x = (x1 + x2) / 2.0
y = (y1 + y2) / 2.0
w = x2 - x1
h = y2 - y1
x = x * dw
w = w * dw
y = y * dh
h = h * dh
file_path_img = os.path.join(output_path,cfolder + '_' + cur_img)
filename, file_extension = os.path.splitext(file_path_img)
with open(file_path_img.replace(file_extension,'.txt'), 'a+') as f:
f.write(' '.join([str(int(cls_indx[cur_cls.index(cls)])), str(float(x)), str(float(y)), str(float(w)), str(float(h))]))
f.write('\n')
print (all_cls)
for img in val_data:
cur_img, cur_id, width, height = img['file_name'],img['id'], img['width'], img['height']
annt_match = []
for cur_annt in all_annts:
if(cur_annt['image_id']) == cur_id:
annt_match.append(cur_annt)
full_img_path = os.path.join(img_path,cur_img)
shutil.copy(full_img_path,os.path.join(val_path,cfolder + '_' + cur_img))
frame = cv2.imread(full_img_path)
for cur_match in annt_match:
bbox = cur_match['bbox']
cls = cls_names[cur_match['category_id']]
if not cls in all_cls:all_cls.append(cls)
x1,y1 = int(bbox[0]),int(bbox[1])
x2, y2 = int(bbox[0])+int(bbox[2]),int(bbox[1]+int(bbox[3]))
dw = 1. / width
dh = 1. / height
x = (x1 + x2) / 2.0
y = (y1 + y2) / 2.0
w = x2 - x1
h = y2 - y1
x = x * dw
w = w * dw
y = y * dh
h = h * dh
file_path_img = os.path.join(val_path,cfolder + '_' + cur_img)
filename, file_extension = os.path.splitext(file_path_img)
with open(file_path_img.replace(file_extension,'.txt'), 'a+') as f:
f.write(' '.join([str(int(cls_indx[cur_cls.index(cls)])), str(float(x)), str(float(y)), str(float(w)), str(float(h))]))
f.write('\n')
def csv2cvat(csv_path, img_full_path,output_path):
annotations = []
images = []
segmentation = []
image_id = 0; det_id = 0; iscrowd = 0;
cls = ['ALLIGATOR', 'BLOCK','TRANSVERSE', 'PATCHING', 'SEALING', 'LONGITUDINAL', 'MANHOLE']
clss = [0,1,2,3,4,5,6]
categories = []
for cl in cls:
cls_id = cls.index(cl)+1
categories.append({"supercategory": "", "id":cls_id, "name":cl})
img_added = []
img_added_ = []
# print (categories)
df = pd.read_csv(csv_path)
files = df['image'].unique()
for curfile in files:
img_added_.append(curfile)
cfilepath = os.path.join(img_full_path, curfile)
print (cfilepath)
im = cv2.imread(cfilepath)
img_height = im.shape[0]
img_width = im.shape[1]
df_cur = df[df['image'] == curfile]
data_configs = df_cur[['cls','score','x1','y1','x2','y2']].values
for data_config in data_configs:
w = (data_config[4] - data_config[2])
h = (data_config[5] - data_config[3])
area = w*h
# print (data_config, area)
bbox = [data_config[2], data_config[3], w, h]
category_id = clss.index(data_config[0]) + 1
cobj = {'segmentation': segmentation, 'category_id': category_id, 'id': det_id, 'area': area,
'iscrowd': iscrowd, 'bbox': bbox, 'image_id': image_id}
annotations.append(cobj)
if not curfile in img_added:
cimgs = {"flickr_url": "", "id": image_id, "date_captured": 0, "width": img_width,
"license": 0, "file_name": curfile.replace('.toml',''),
"coco_url": "", "height": img_height}
images.append(cimgs)
img_added.append(curfile)
det_id += 1
image_id+=1
df_json = {}
df_json['info'] = {"contributor": "", "year": "", "description": "", "version": "", "url": "", "date_created": ""}
df_json['annotations'] = annotations
df_json['images'] = images
df_json['categories'] = categories
df_json['licenses'] = [{"url": "", "id": 0, "name": ""}]
with open(output_path, 'w') as fp:
json.dump(df_json, fp)
def cvat2yolo_(json_file,img_path, output_path, val_path):
if not os.path.isdir(output_path):
os.makedirs(output_path)
os.makedirs(val_path)
with open(json_file) as f:
json_data = json.load(f)
img_data = json_data['images']
all_annts = json_data['annotations']
# print (json_data['categories'])
cls_names = {}
for catv in json_data['categories']:
cls_names[catv['id']] = catv['name']
print (cls_names)
all_cls = []
cls_indx = list(range(0,len(cls_names)))
cur_cls = list(cls_names.values())
print ('total number of classes: {}'.format(len(cur_cls)))
# val_data = random.sample(img_data,int(len(img_data)*0.2))
# train_data = list(set(img_data).difference(val_data))
random.shuffle(img_data)
val_data = img_data[0:int(len(img_data)*0.2)]
train_data = img_data[int(len(img_data)*0.2):]
# print (cur_cls)
for img in train_data:
cur_img, cur_id, width, height = img['file_name'],img['id'], img['width'], img['height']
annt_match = []
for cur_annt in all_annts:
if(cur_annt['image_id']) == cur_id:
annt_match.append(cur_annt)
full_img_path = os.path.join(img_path,cur_img)
shutil.copy(full_img_path,os.path.join(output_path,cur_img))
frame = cv2.imread(full_img_path)
for cur_match in annt_match:
bbox = cur_match['bbox']
cls = cls_names[cur_match['category_id']]
if not cls in all_cls:all_cls.append(cls)
x1,y1 = int(bbox[0]),int(bbox[1])
x2, y2 = int(bbox[0])+int(bbox[2]),int(bbox[1]+int(bbox[3]))
dw = 1. / width
dh = 1. / height
x = (x1 + x2) / 2.0
y = (y1 + y2) / 2.0
w = x2 - x1
h = y2 - y1
x = x * dw
w = w * dw
y = y * dh
h = h * dh
file_path_img = os.path.join(output_path,cur_img)
filename, file_extension = os.path.splitext(file_path_img)
with open(file_path_img.replace(file_extension,'.txt'), 'a+') as f:
f.write(' '.join([str(int(cls_indx[cur_cls.index(cls)])), str(float(x)), str(float(y)), str(float(w)), str(float(h))]))
f.write('\n')
print (all_cls)
for img in val_data:
cur_img, cur_id, width, height = img['file_name'],img['id'], img['width'], img['height']
annt_match = []
for cur_annt in all_annts:
if(cur_annt['image_id']) == cur_id:
annt_match.append(cur_annt)
full_img_path = os.path.join(img_path,cur_img)
shutil.copy(full_img_path,os.path.join(val_path,cur_img))
frame = cv2.imread(full_img_path)
for cur_match in annt_match:
bbox = cur_match['bbox']
cls = cls_names[cur_match['category_id']]
if not cls in all_cls:all_cls.append(cls)
x1,y1 = int(bbox[0]),int(bbox[1])
x2, y2 = int(bbox[0])+int(bbox[2]),int(bbox[1]+int(bbox[3]))
dw = 1. / width
dh = 1. / height
x = (x1 + x2) / 2.0
y = (y1 + y2) / 2.0
w = x2 - x1
h = y2 - y1
x = x * dw
w = w * dw
y = y * dh
h = h * dh
file_path_img = os.path.join(val_path,cur_img)
filename, file_extension = os.path.splitext(file_path_img)
with open(file_path_img.replace(file_extension,'.txt'), 'a+') as f:
f.write(' '.join([str(int(cls_indx[cur_cls.index(cls)])), str(float(x)), str(float(y)), str(float(w)), str(float(h))]))
f.write('\n')