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DetVisGUI.py
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# __author__ = 'ChienHung Chen in Academia Sinica IIS'
import argparse
import itertools
import json
import os
import pickle
import xml.etree.ElementTree as ET
from tkinter import (END, Button, Checkbutton, E, Entry, IntVar, Label,
Listbox, Menu, N, S, Scrollbar, StringVar, Tk, W, ttk)
import cv2
import matplotlib
import mmcv
import numpy as np
import platform
import pycocotools.mask as maskUtils
from PIL import Image, ImageTk
from tqdm import trange
matplotlib.use('TkAgg')
def parse_args():
parser = argparse.ArgumentParser(description='DetVisGUI')
parser.add_argument('config', help='config file path')
parser.add_argument('--det_file',
default='',
help='detection results file path')
parser.add_argument(
'--stage',
default='val',
choices=['train', 'val', 'test'],
help='stage')
parser.add_argument(
'--no_gt',
action='store_true',
default=False,
help='test images without groundtruth')
parser.add_argument(
'--det_box_color', default=(255, 255, 0), help='detection box color')
parser.add_argument(
'--gt_box_color',
default=(255, 255, 255),
help='groundtruth box color')
parser.add_argument('--output', default='output', help='image save folder')
args = parser.parse_args()
return args
class COCO_dataset:
def __init__(self, cfg, args):
self.dataset = 'COCO'
self.img_root = getattr(cfg.data, args.stage).img_prefix
self.anno_root = getattr(cfg.data, args.stage).ann_file
self.det_file = args.det_file
self.has_anno = not args.no_gt
self.mask = False
# according json to get category, image list, and annotations.
self.category, self.img_list, self.total_annotations, self.cat2idx, \
self.img2idx = self.parse_json(self.anno_root, self.has_anno)
self.aug_category = aug_category(self.category)
self.results = self.get_det_results() if self.det_file != '' else None
if self.det_file != '':
self.img_det = {
self.img_list[i]: self.results[:, i]
for i in range(len(self.img_list))
}
def parse_json(self, train_anno, has_anno):
with open(train_anno) as f:
data = json.load(f)
category = [c['name'] for c in data['categories']] # 80 classes
if 'annotations' not in data.keys():
has_anno = False
self.has_anno = False
if has_anno:
annotations = data['annotations']
images = data['images']
category_dict = {c['id']: c['name'] for c in data['categories']}
cat2idx = {c['id']: i for i, c in enumerate(data['categories'])}
max_category_id = max(category_dict.keys())
# id to image mapping
image_dict = {}
img_list = list()
img_id_list = list()
for image in images:
key = image['id']
image_dict[key] = [
image['file_name'], image['width'], image['height']
]
img_list.append(image['file_name'])
img_id_list.append(key)
img2idx = {x:i for i, x in enumerate(img_id_list)}
category_count = [0 for _ in range(max_category_id + 1)]
total_annotations = {}
if has_anno:
for a in annotations:
image_name = image_dict[a['image_id']][0].replace('.jpg', '')
width = image_dict[a['image_id']][1]
height = image_dict[a['image_id']][2]
idx = a['category_id']
single_ann = []
single_ann.append(category_dict[idx])
single_ann.extend(list(map(int, a['bbox'])))
single_ann.extend([width, height])
if image_name not in total_annotations:
total_annotations[image_name] = []
category_count[idx] += 1
total_annotations[image_name].append(single_ann)
print('\n==============[ {} json info ]=============='.format(
self.dataset))
print('Total Annotations: {}'.format(len(annotations)))
print('Total Image : {}'.format(len(images)))
print('Annotated Image : {}'.format(len(total_annotations)))
print('Total Category : {}'.format(len(category)))
print('----------------------------')
print('{:^20}| count'.format('class'))
print('----------------------------')
for idx, cat_idx in enumerate(cat2idx):
c = category[idx]
cnt = category_count[cat_idx]
if cnt != 0:
print('{:^20}| {}'.format(c, cnt))
print()
return category, img_list, total_annotations, cat2idx, img2idx
def get_det_results(self):
det_file = self.det_file
if det_file != '':
if det_file.endswith('.pkl'):
with open(det_file, 'rb') as f:
det_results = np.asarray(
pickle.load(f), dtype=object) # [(bg + cls), images]
# dim should be (class, image)
if len(det_results.shape) == 2:
det_results = np.transpose(det_results, (1, 0))
# dim should be (class, image, mask)
elif len(det_results.shape) == 3:
det_results = np.transpose(det_results, (2, 0, 1))
self.mask = True
elif det_file.endswith('.json'):
with open(det_file) as f:
json_results = json.load(f)
if 'segmentation' in json_results[0]:
self.mask = True
det_results = [[[np.empty((0, 5)),[]] for _ in range(len(self.img_list))] for _ in range(len(self.cat2idx))]
for res in json_results:
img_idx = self.img2idx[res['image_id']]
cat_idx = self.cat2idx[res['category_id']]
x, y, w, h = res['bbox']
det_results[cat_idx][img_idx][0] = np.concatenate(
(det_results[cat_idx][img_idx][0], np.asarray([x, y, x+w, y+h, res['score']]).reshape(1, -1))
)
det_results[cat_idx][img_idx][1].append(res['segmentation'])
elif 'bbox' in json_results[0]:
det_results = [[np.empty((0, 5)) for _ in range(len(self.img_list))] for _ in range(len(self.cat2idx))]
for res in json_results:
img_idx = self.img2idx[res['image_id']]
cat_idx = self.cat2idx[res['category_id']]
x, y, w, h = res['bbox']
det_results[cat_idx][img_idx] = np.concatenate(
(det_results[cat_idx][img_idx], np.asarray([x, y, x+w, y+h, res['score']]).reshape(1, -1))
)
det_results = np.asarray(det_results, dtype=object)
return det_results
else:
return None
def get_img_by_name(self, name):
img = Image.open(os.path.join(self.img_root, name)).convert('RGB')
return img
def get_img_by_index(self, idx):
img = Image.open(os.path.join(self.img_root,
self.img_list[idx])).convert('RGB')
return img
def get_singleImg_gt(self, name):
if name.replace('.jpg', '') not in self.total_annotations.keys():
print('There are no annotations in %s.' % name)
return []
else:
return self.total_annotations[name.replace('.jpg', '')]
def get_singleImg_dets(self, name):
return self.img_det[name]
# pascal voc dataset
class VOC_dataset:
def __init__(self, cfg, args):
self.dataset = 'PASCAL VOC'
self.det_file = args.det_file
self.txt = getattr(cfg.data, args.stage).ann_file
self.anno_root = os.path.join(
getattr(cfg.data, args.stage).img_prefix, 'Annotations')
self.img_root = os.path.join(
getattr(cfg.data, args.stage).img_prefix, 'JPEGImages')
self.has_anno = not args.no_gt
self.mask = False
# according txt to get image list
self.img_list = self.get_img_list()
self.results = self.get_det_results() if self.det_file != '' else None
self.aug_category = aug_category([
'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
])
if self.det_file != '':
self.img_det = {
self.img_list[i]: self.results[:, i]
for i in range(len(self.img_list))
}
def get_img_list(self):
with open(self.txt, 'r') as f:
data = f.readlines()
return [x.strip() + '.jpg' for x in data]
def get_det_results(self):
det_file = self.det_file
if det_file != '':
with open(det_file, 'rb') as f:
det_results = np.asarray(
pickle.load(f), dtype=object) # [(bg + cls), images]
# dim should be (class, image), mmdetection format: (image, class)
det_results = np.transpose(det_results, (1, 0))
return det_results
else:
return None
def get_img_by_name(self, name):
img = Image.open(os.path.join(self.img_root, name)).convert('RGB')
return img
def get_img_by_index(self, idx):
img = Image.open(os.path.join(self.img_root,
self.img_list[idx])).convert('RGB')
return img
def get_singleImg_gt(self, name): # get annotations by image name
xml_path = os.path.join(self.anno_root,
name.replace('.jpg', '') + '.xml')
tree = ET.parse(xml_path)
root = tree.getroot()
img_anns = []
for obj in root.findall('object'):
single_ann = []
name = obj.find('name').text
bndbox = obj.find('bndbox')
bbox = [
int(bndbox.find('xmin').text),
int(bndbox.find('ymin').text),
int(bndbox.find('xmax').text) - int(bndbox.find('xmin').text),
int(bndbox.find('ymax').text) - int(bndbox.find('ymin').text)
]
single_ann.append(name)
single_ann.extend(bbox)
img_anns.append(single_ann)
return img_anns
def get_singleImg_dets(self, name):
return self.img_det[name]
# main GUI
class vis_tool:
def __init__(self):
self.args = parse_args()
cfg = mmcv.Config.fromfile(self.args.config)
self.window = Tk()
self.menubar = Menu(self.window)
self.info = StringVar()
self.info_label = Label(
self.window, bg='yellow', width=4, textvariable=self.info)
self.listBox_img = Listbox(
self.window, width=50, height=25, font=('Times New Roman', 10))
self.listBox_obj = Listbox(
self.window, width=50, height=12, font=('Times New Roman', 10))
self.scrollbar_img = Scrollbar(
self.window, width=15, orient='vertical')
self.scrollbar_obj = Scrollbar(
self.window, width=15, orient='vertical')
self.listBox_img_info = StringVar()
self.listBox_img_label = Label(
self.window,
font=('Arial', 11),
bg='yellow',
width=4,
height=1,
textvariable=self.listBox_img_info)
self.listBox_obj_info = StringVar()
self.listBox_obj_label1 = Label(
self.window,
font=('Arial', 11),
bg='yellow',
width=4,
height=1,
textvariable=self.listBox_obj_info)
self.listBox_obj_label2 = Label(
self.window,
font=('Arial', 11),
bg='yellow',
width=4,
height=1,
text='Object Class : Score (IoU)')
if cfg.dataset_type == 'VOCDataset':
self.data_info = VOC_dataset(cfg, self.args)
elif cfg.dataset_type == 'CocoDataset':
self.data_info = COCO_dataset(cfg, self.args)
self.info.set('DATASET: {}'.format(self.data_info.dataset))
# load image and show it on the window
self.img = self.data_info.get_img_by_index(0)
self.photo = ImageTk.PhotoImage(self.img)
self.label_img = Label(self.window, image=self.photo)
self.show_det_txt = IntVar(value=1)
self.checkbn_det_txt = Checkbutton(
self.window,
text='Text',
font=('Arial', 10, 'bold'),
variable=self.show_det_txt,
command=self.change_img,
fg='#0000FF')
self.show_dets = IntVar(value=1)
self.checkbn_det = Checkbutton(
self.window,
text='Detections',
font=('Arial', 10, 'bold'),
variable=self.show_dets,
command=self.change_img,
fg='#0000FF')
self.show_gt_txt = IntVar(value=1)
self.checkbn_gt_txt = Checkbutton(
self.window,
text='Text',
font=('Arial', 10, 'bold'),
variable=self.show_gt_txt,
command=self.change_img,
fg='#FF8C00')
self.show_gts = IntVar(value=1)
self.checkbn_gt = Checkbutton(
self.window,
text='Groundtruth',
font=('Arial', 10, 'bold'),
variable=self.show_gts,
command=self.change_img,
fg='#FF8C00')
self.combo_label = Label(
self.window,
bg='yellow',
width=10,
height=1,
text='Show Category',
font=('Arial', 11))
self.combo_category = ttk.Combobox(
self.window,
font=('Arial', 11),
values=self.data_info.aug_category.combo_list)
self.combo_category.current(0)
self.th_label = Label(
self.window,
font=('Arial', 11),
bg='yellow',
width=10,
height=1,
text='Score Threshold')
self.threshold = np.float32(0.5)
self.th_entry = Entry(
self.window,
font=('Arial', 11),
width=10,
textvariable=StringVar(self.window, value=str(self.threshold)))
self.th_button = Button(
self.window, text='Enter', height=1, command=self.change_threshold)
self.iou_th_label = Label(
self.window,
font=('Arial', 11),
bg='yellow',
width=10,
height=1,
text='IoU Threshold')
self.iou_threshold = np.float32(0.5)
self.iou_th_entry = Entry(
self.window,
font=('Arial', 11),
width=10,
textvariable=StringVar(self.window, value=str(self.iou_threshold)))
self.iou_th_button = Button(
self.window, text='Enter', height=1, command=self.change_iou_threshold)
self.find_label = Label(
self.window,
font=('Arial', 11),
bg='yellow',
width=10,
height=1,
text='find')
self.find_name = ''
self.find_entry = Entry(
self.window,
font=('Arial', 11),
width=10,
textvariable=StringVar(self.window, value=str(self.find_name)))
self.find_button = Button(
self.window, text='Enter', height=1, command=self.findname)
self.listBox_img_idx = 0
# ====== ohter attribute ======
self.img_name = ''
self.show_img = None
self.output = self.args.output
if not os.path.isdir(self.output):
os.makedirs(self.output)
self.img_list = self.data_info.img_list
# flag for find/threshold button switch focused element
self.button_clicked = False
def change_threshold(self, event=None):
try:
self.threshold = np.float32(self.th_entry.get())
self.change_img()
# after changing threshold, focus on listBox for easy control
if self.window.focus_get() == self.listBox_obj:
self.listBox_obj.focus()
else:
self.listBox_img.focus()
self.button_clicked = True
except ValueError:
self.window.title('Please enter a number as score threshold.')
def change_iou_threshold(self, event=None):
try:
self.iou_threshold = np.float32(self.iou_th_entry.get())
self.change_img()
# after changing threshold, focus on listBox for easy control
if self.window.focus_get() == self.listBox_obj:
self.listBox_obj.focus()
else:
self.listBox_img.focus()
self.button_clicked = True
except ValueError:
self.window.title("Please enter a number as IoU threshold.")
# draw groundtruth
def draw_gt_boxes(self, img, objs):
for obj in objs:
cls_name = obj[0]
# according combobox to decide whether to plot this category
if self.combo_category.get() == 'All':
show_category = self.data_info.aug_category.category
else:
show_category = [self.combo_category.get()]
if cls_name not in show_category:
continue
box = obj[1:]
xmin = max(box[0], 0)
ymin = max(box[1], 0)
xmax = min(box[0] + box[2], self.img_width)
ymax = min(box[1] + box[3], self.img_height)
font = cv2.FONT_HERSHEY_SIMPLEX
if self.show_gt_txt.get():
if ymax + 30 >= self.img_height:
cv2.rectangle(img, (xmin, ymin),
(xmin + len(cls_name) * 10, int(ymin - 20)),
(255, 140, 0), cv2.FILLED)
cv2.putText(img, cls_name, (xmin, int(ymin - 5)), font,
0.5, (255, 255, 255), 1)
else:
cv2.rectangle(img, (xmin, ymax),
(xmin + len(cls_name) * 10, int(ymax + 20)),
(255, 140, 0), cv2.FILLED)
cv2.putText(img, cls_name, (xmin, int(ymax + 15)), font,
0.5, (255, 255, 255), 1)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax),
self.args.gt_box_color, 1)
return img
def get_iou(self, det):
iou = np.zeros_like(det)
GT = self.data_info.get_singleImg_gt(self.img_name)
for idx, cls_objs in enumerate(det):
category = self.data_info.aug_category.category[idx]
BBGT = []
for t in GT:
if not t[0] == category: continue
BBGT.append([t[1], t[2], t[1] + t[3], t[2] + t[4]])
BBGT = np.asarray(BBGT)
d = [0] * len(BBGT) # for check 1 GT map to several det
confidence = cls_objs[:, 4]
BB = cls_objs[:, 0:4] # bounding box
# sort by confidence
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
# for returning original order
ind_table = {i: sorted_ind[i] for i in range(len(sorted_ind))}
iou[idx] = np.zeros(len(BB))
if len(BBGT) > 0:
for i in range(len(BB)):
bb = BB[i, :]
# compute overlaps
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) * (BBGT[:, 3] - BBGT[:, 1] + 1.) -
inters)
overlaps = inters / uni
ovmax = np.max(overlaps) # max overlaps with all gt
jmax = np.argmax(overlaps)
if ovmax > self.iou_threshold:
if not d[jmax]:
d[jmax] = 1
else: # multiple bounding boxes map to one gt
ovmax = -ovmax
iou[idx][ind_table[i]] = ovmax # return to unsorted order
return iou
def draw_all_det_boxes(self, img, single_detection):
if self.data_info.has_anno:
self.iou = self.get_iou(single_detection)
for idx, cls_objs in enumerate(single_detection):
category = self.data_info.aug_category.category[idx]
if self.combo_category.get() == 'All':
show_category = self.data_info.aug_category.category
else:
show_category = [self.combo_category.get()]
if category not in show_category:
continue
for obj_idx, obj in enumerate(cls_objs):
[score, box] = [round(obj[4], 2), obj[:4]]
if score >= self.threshold:
box = list(map(int, list(map(round, box))))
xmin = max(box[0], 0)
ymin = max(box[1], 0)
xmax = min(box[2], self.img_width)
ymax = min(box[3], self.img_height)
if not self.data_info.has_anno or \
self.iou[idx][obj_idx] >= self.iou_threshold:
color = self.args.det_box_color
else:
color = (255, 0, 0)
if self.show_det_txt.get():
font = cv2.FONT_HERSHEY_SIMPLEX
text = category + ' : ' + str(score)
if ymax + 30 >= self.img_height:
cv2.rectangle(
img, (xmin, ymin),
(xmin + len(text) * 9, int(ymin - 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymin - 5)), font,
0.5, (255, 255, 255), 1)
else:
cv2.rectangle(
img, (xmin, ymax),
(xmin + len(text) * 9, int(ymax + 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymax + 15)),
font, 0.5, (255, 255, 255), 1)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2)
return img
def draw_all_det_boxes_masks(self, img, single_detection):
img = np.require(img, requirements=['W'])
boxes, masks = single_detection
# draw segmentation masks
# reference mmdetection/mmdet/models/detectors/base.py
if self.combo_category.get() != 'All':
show_idx = self.data_info.aug_category.category.index(
self.combo_category.get())
masks = np.asarray([masks[show_idx]])
boxes = np.asarray([boxes[show_idx]])
category = self.data_info.aug_category.category[show_idx]
segms = list(itertools.chain(*masks))
bboxes = np.vstack(boxes)
inds = np.where(np.round(bboxes[:, -1], 2) >= self.threshold)[0]
self.color_list = []
for i in inds:
color_mask = np.random.randint(0, 256, (1, 3), dtype=np.uint8)
if type(segms[0]) == np.ndarray:
mask = segms[i]
elif type(segms[0]) == dict:
mask = maskUtils.decode(segms[i]).astype(np.bool)
img[mask] = img[mask] * 0.5 + color_mask * 0.5
self.color_list.append('#%02x%02x%02x' % tuple(color_mask[0]))
if self.data_info.has_anno:
boxes2, _ = single_detection
self.iou = self.get_iou(boxes2)
if self.combo_category.get() != 'All':
iou = np.asarray([self.iou[show_idx]])
else:
iou = self.iou
# draw bounding box
for idx, cls_objs in enumerate(boxes):
if self.combo_category.get() == 'All':
category = self.data_info.aug_category.category[idx]
for obj_idx, obj in enumerate(cls_objs):
[score, box] = [round(obj[4], 2), obj[:4]]
if score >= self.threshold:
box = list(map(int, list(map(round, box))))
xmin = max(box[0], 0)
ymin = max(box[1], 0)
xmax = min(box[2], self.img_width)
ymax = min(box[3], self.img_height)
if not self.data_info.has_anno or \
iou[idx][obj_idx] >= self.iou_threshold:
color = self.args.det_box_color
else:
color = (255, 0, 0)
if self.show_det_txt.get():
font = cv2.FONT_HERSHEY_SIMPLEX
text = category + ' : ' + str(score)
if ymax + 30 >= self.img_height:
cv2.rectangle(
img, (xmin, ymin),
(xmin + len(text) * 9, int(ymin - 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymin - 5)), font,
0.5, (255, 255, 255), 1)
else:
cv2.rectangle(
img, (xmin, ymax),
(xmin + len(text) * 9, int(ymax + 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymax + 15)),
font, 0.5, (255, 255, 255), 1)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2)
return img
def change_img(self, event=None):
if len(self.listBox_img.curselection()) != 0:
self.listBox_img_idx = self.listBox_img.curselection()[0]
self.listBox_img_info.set('Image {:6} / {:6}'.format(
self.listBox_img_idx + 1, self.listBox_img.size()))
name = self.listBox_img.get(self.listBox_img_idx)
self.window.title('DATASET : ' + self.data_info.dataset + ' ' + name)
img = self.data_info.get_img_by_name(name)
self.img_width, self.img_height = img.width, img.height
img = np.asarray(img)
self.img_name = name
self.img = img
if self.data_info.has_anno and self.show_gts.get():
objs = self.data_info.get_singleImg_gt(name)
img = self.draw_gt_boxes(img, objs)
if self.data_info.results is not None and self.show_dets.get():
if self.data_info.mask is False:
dets = self.data_info.get_singleImg_dets(name)
img = self.draw_all_det_boxes(img, dets)
else:
dets = self.data_info.get_singleImg_dets(name).transpose(
(1, 0))
img = self.draw_all_det_boxes_masks(img, dets)
self.clear_add_listBox_obj()
self.show_img = img
img = Image.fromarray(img)
img = self.scale_img(img)
self.photo = ImageTk.PhotoImage(img)
self.label_img.config(image=self.photo)
self.window.update_idletasks()
if self.img_name in os.listdir(self.output):
self.listBox_img_label.config(bg='#CCFF99')
else:
self.listBox_img_label.config(bg='yellow')
def draw_one_det_boxes(self, img, single_detection, selected_idx=-1):
idx_counter = 0
for idx, cls_objs in enumerate(single_detection):
category = self.data_info.aug_category.category[idx]
if self.combo_category.get() == 'All':
show_category = self.data_info.aug_category.category
else:
show_category = [self.combo_category.get()]
if category not in show_category:
continue
for obj_idx, obj in enumerate(cls_objs):
[score, box] = [round(obj[4], 2), obj[:4]]
if score >= self.threshold:
if idx_counter == selected_idx:
box = list(map(int, list(map(round, box))))
xmin = max(box[0], 0)
ymin = max(box[1], 0)
xmax = min(box[2], self.img_width)
ymax = min(box[3], self.img_height)
if not self.data_info.has_anno or \
self.iou[idx][obj_idx] >= self.iou_threshold:
color = self.args.det_box_color
else:
color = (255, 0, 0)
if self.show_det_txt.get():
font = cv2.FONT_HERSHEY_SIMPLEX
text = category + ' : ' + str(score)
if ymax + 30 >= self.img_height:
cv2.rectangle(
img, (xmin, ymin),
(xmin + len(text) * 9, int(ymin - 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymin - 5)),
font, 0.5, (255, 255, 255), 1)
else:
cv2.rectangle(
img, (xmin, ymax),
(xmin + len(text) * 9, int(ymax + 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymax + 15)),
font, 0.5, (255, 255, 255), 1)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2)
return img
else:
idx_counter += 1
def draw_one_det_boxes_masks(self, img, single_detection, selected_idx=-1):
img = np.require(img, requirements=['W'])
boxes, masks = single_detection
# draw segmentation masks
# reference mmdetection/mmdet/models/detectors/base.py
if self.combo_category.get() != 'All':
show_idx = self.data_info.aug_category.category.index(
self.combo_category.get())
category = self.data_info.aug_category.category[
show_idx] # fixed category
masks = np.asarray([masks[show_idx]])
boxes = np.asarray([boxes[show_idx]])
segms = list(itertools.chain(*masks))
bboxes = np.vstack(boxes)
inds = np.where(np.round(bboxes[:, -1], 2) >= self.threshold)[0]
self.color_list = []
for inds_idx, i in enumerate(inds):
if inds_idx == selected_idx:
color_mask = np.random.randint(0, 256, (1, 3), dtype=np.uint8)
if type(segms[0]) == np.ndarray:
mask = segms[i]
elif type(segms[0]) == dict:
mask = maskUtils.decode(segms[i]).astype(np.bool)
img[mask] = img[mask] * 0.5 + color_mask * 0.5
self.color_list.append('#%02x%02x%02x' % tuple(color_mask[0]))
if self.data_info.has_anno:
if self.combo_category.get() != 'All':
iou = np.asarray([self.iou[show_idx]])
else:
iou = self.iou
# draw bounding box
idx_counter = 0
for idx, cls_objs in enumerate(boxes):
if self.combo_category.get() == 'All':
category = self.data_info.aug_category.category[idx]
for obj_idx, obj in enumerate(cls_objs):
[score, box] = [round(obj[4], 2), obj[:4]]
if score >= self.threshold:
if idx_counter == selected_idx:
box = list(map(int, list(map(round, box))))
xmin = max(box[0], 0)
ymin = max(box[1], 0)
xmax = min(box[2], self.img_width)
ymax = min(box[3], self.img_height)
if not self.data_info.has_anno or \
iou[idx][obj_idx] >= self.iou_threshold:
color = self.args.det_box_color
else:
color = (255, 0, 0)
if self.show_det_txt.get():
font = cv2.FONT_HERSHEY_SIMPLEX
text = category + ' : ' + str(score)
if ymax + 30 >= self.img_height:
cv2.rectangle(
img, (xmin, ymin),
(xmin + len(text) * 9, int(ymin - 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymin - 5)),
font, 0.5, (255, 255, 255), 1)
else:
cv2.rectangle(
img, (xmin, ymax),
(xmin + len(text) * 9, int(ymax + 20)),
(0, 0, 255), cv2.FILLED)
cv2.putText(img, text, (xmin, int(ymax + 15)),
font, 0.5, (255, 255, 255), 1)
cv2.rectangle(img, (xmin, ymin), (xmax, ymax),
color, 2)
return img
else:
idx_counter += 1
# plot only one object
def change_obj(self, event=None):
if len(self.listBox_obj.curselection()) == 0:
self.listBox_img.focus()
return
else:
listBox_obj_idx = self.listBox_obj.curselection()[0]
self.listBox_obj_info.set('Detected Object : {:4} / {:4}'.format(
listBox_obj_idx + 1, self.listBox_obj.size()))
name = self.listBox_img.get(self.listBox_img_idx)
img = self.data_info.get_img_by_name(name)
self.img_width, self.img_height = img.width, img.height
img = np.asarray(img)
self.img_name = name
self.img = img
if self.data_info.has_anno and self.show_gts.get():
objs = self.data_info.get_singleImg_gt(name)
img = self.draw_gt_boxes(img, objs)
if self.data_info.results is not None and self.show_dets.get():
if self.data_info.mask is False:
dets = self.data_info.get_singleImg_dets(name)
img = self.draw_one_det_boxes(img, dets, listBox_obj_idx)
else:
dets = self.data_info.get_singleImg_dets(name).transpose(
(1, 0))
img = self.draw_one_det_boxes_masks(img, dets, listBox_obj_idx)
self.show_img = img
img = Image.fromarray(img)
img = self.scale_img(img)
self.photo = ImageTk.PhotoImage(img)
self.label_img.config(image=self.photo)
self.window.update_idletasks()
if self.img_name in os.listdir(self.output):
self.listBox_img_label.config(bg='#CCFF99')
else:
self.listBox_img_label.config(bg='yellow')
# ============================================
def scale_img(self, img):
[s_w, s_h] = [1, 1]
# if window size is (1920, 1080),
# the default max image size is (1440, 810)
(fix_width, fix_height) = (1440, 810)
# change image size according to window size
if self.window.winfo_width() != 1:
fix_width = (