def main(path_results, dataset_name, model_num, score, iou, num_annotation_file=1,
plot=True, full=True, best_weights=True):
model_folder = train_utils.get_model_path(path_results, dataset_name, model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
class_names = ktrain.get_classes(train_params['path_classes'])
# train_params['path_annotations'][1] = train_params['path_annotations'][1].replace('_sk10', '_sk20')
annotations_file = train_params['path_annotations'][num_annotation_file]
if 'adl' in annotations_file and train_params.get('size_suffix', '') != '':
annotations_file = annotations_file.replace(train_params.get('size_suffix', ''), '')
# annotations_file = annotations_file.replace('.txt', '_pr416.txt')
print(' * Exploring:', annotations_file)
if best_weights:
preds_filename = '{}preds_{}_score{}_iou{}.json'.format(model_folder, annotations_file.split('/')[-1][:-4], score, iou)
eval_filename = '{}stats_{}_score{}_iou{}.json'.format(model_folder, annotations_file.split('/')[-1][:-4], score, iou)
else:
preds_filename = '{}preds_stage2_{}_score{}_iou{}.json'.format(model_folder, annotations_file.split('/')[-1][:-4], score, iou)
eval_filename = '{}stats_stage2_{}_score{}_iou{}.json'.format(model_folder, annotations_file.split('/')[-1][:-4], score, iou)
# print(preds_filename)
print('='*80)
train_diff, train_loss, val_loss = get_train_resume(model_folder)
_ = prediction_utils.predict_and_store_from_annotations(model_folder, train_params, annotations_file, preds_filename, score, iou, best_weights=best_weights)
occurrences = None
eval_stats, videos = get_full_evaluation(annotations_file, preds_filename, class_names, full)
# resume = get_excel_resume(model_folder, train_params, train_loss, val_loss, eval_stats, train_diff, fps, score, iou)
plot_prediction_resume(eval_stats, videos, class_names, 'video', annotations_file, model_num, plot);
occurrences = plot_prediction_resume(eval_stats, videos, class_names, 'class', annotations_file, model_num, plot)
# print(resume)
return model_folder, class_names, videos, occurrences, eval_stats, train_params, (train_loss, val_loss, train_diff)
path_results = '/mnt/hdd/egocentric_results/'
dataset_name = 'adl'
path_dataset = '/home/asabater/projects/ADL_dataset/'
#num_ann, score = 0, MIN_SCORE
num_ann, score = 1, 0
min_score = 0.3
model_nums = {45:'320', 56:'416', 44:'608'}
#model_nums = {18:'320', 57:'416', 17:'608'}
preds_num = {}
for model_num, label in model_nums.items():
model_folder = train_utils.get_model_path(path_results, dataset_name, model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
annotations_file = train_params['path_annotations'][num_ann]
preds_filename = '{}preds_stage2_{}_score{}_iou{}.json'.format(model_folder, annotations_file.split('/')[-1][:-4], score, 0.5)
preds = json.load(open(preds_filename, 'r'))
preds = [ p for p in preds if p['score'] >= min_score ]
preds_num[label] = pd.DataFrame(preds)
# %%
#preds_count = { k:pd.DataFrame(v.groupby('image_id').score.sum() / v.groupby('image_id').score.size()) for k,v in preds_num.items() }
#for k,v in preds_count.items(): preds_count[k].columns = [k]
preds_count = { k:pd.DataFrame(v.groupby('image_id').size(), columns=[k]) for k,v in preds_num.items() }
def main():
import os
os.chdir(os.getcwd() + '/..')
# %%
import train_utils
import pandas as pd
import sys
sys.path.append('keras_yolo3/')
sys.path.append('keras_yolo3/yolo3/')
import keras_yolo3.train as ktrain
##path_dataset = '/mnt/hdd/datasets/VOC/'
#path_results = '/mnt/hdd/egocentric_results/'
##path_classes = './dataset_scripts/coco/coco_classes.txt'
#path_dataset = '/mnt/hdd/datasets/coco/'
#path_classes = './dataset_scripts/adl/adl_classes_v3_8.txt'
#
##gt_filename = '/home/asabater/projects/Egocentric-object-detection/dataset_scripts/coco/annotations_coco_val_coco.json'
##gt = json.load(open(gt_filename, 'r'))['annotations']
#
#score = 0.4
#gt_filename = path_results + 'default/voc_yolo_model_0/preds_annotations_coco_val_score{}_iou.json'.format(score)
##gt_filename = path_results + 'adl/0414_2252_model_20/preds_annotations_adl_val_v3_8_r_fd10_fsn1_score0_iou0.5.json'
##gt_filename = path_results + 'adl/0414_2252_model_20/preds_annotations_adl_train_v3_8_r_fd10_fsn1_score5e-05_iou0.5.json'
#gt = json.load(open(gt_filename, 'r'))
#path_classes = './dataset_scripts/voc/voc_classes.txt'
#path_dataset = '/mnt/hdd/datasets/VOC/'
#score = 0.4
#gt_filename = path_results + 'voc/0326_1706_model_0/preds_annotations_voc_val_score{}_iou.json'.format(score)
#gt = json.load(open(gt_filename, 'r'))
path_results = '/mnt/hdd/egocentric_results/'
dataset_name = 'adl'
path_dataset = '/home/asabater/projects/ADL_dataset/'
model_num = 62
model_folder = train_utils.get_model_path(path_results, dataset_name,
model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
annotations_file = train_params['path_annotations'][1]
preds_filename = '{}preds_stage2_{}_score{}_iou{}.json'.format(
model_folder,
annotations_file.split('/')[-1][:-4], 0, 0.5)
gt = json.load(open(preds_filename, 'r'))
# %%
preds = pd.DataFrame([p for p in gt if p['score'] >= 0.3])
preds = [{
'image_id': i,
'bboxes': g.bbox.tolist(),
'category_id': g.category_id.tolist()
} for i, g in preds.groupby('image_id')]
# %%
from playground.test_annotations import print_annotations
class_names = ktrain.get_classes(train_params['path_classes'])
hsv_tuples = [(x / len(class_names), 1., 1.)
for x in range(len(class_names))]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
colors))
preds = preds[random.randint(0, len(preds)):]
for pred in preds:
sample = '{}{}.jpg'.format(path_dataset, pred['image_id'])
for bbox, cat in zip(pred['bboxes'], pred['category_id']):
sample += ' {},{},{},{},{}'.format(bbox[0], bbox[1],
bbox[0] + bbox[2],
bbox[1] + bbox[3], cat)
print(sample)
pass
# result = print_annotations(sample, 0.7, class_names)
result = print_annotations(sample, class_names, colors, perc=0.7)
cv2.imshow("result", result)
if cv2.waitKey(200) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def main_v2(path_results, dataset_name, model_num, input_shape=None, eval_train=True, full=False):
model_folder = train_utils.get_model_path(path_results, dataset_name, model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
class_names = ktrain.get_classes(train_params['path_classes'])
annotation_files = train_params['path_annotations']
# annotation_files[1] = annotation_files[1].replace('_sk20', '_sk3')
# train_params['eval_val_score'] = 0.05
if input_shape is not None:
train_params['input_shape'] = input_shape
# train_params['eval_val_score'] = 0.005
# train_params['eval_train_score'] = 0.005
# Pred and eval val bw
_, preds_filename_val_bw = prediction_utils.predict_and_store_from_annotations(model_folder,
train_params, annotation_files[1], model_folder,
image_size=train_params['input_shape'],
score=train_params.get('eval_val_score'), nms_iou=0.5,
best_weights=True, raw_eval='def')
eval_stats_val_bw, videos = get_full_evaluation(annotation_files[1], preds_filename_val_bw, class_names, full)
# Pred and eval val st2
_, preds_filename_val_st2 = prediction_utils.predict_and_store_from_annotations(model_folder,
train_params, annotation_files[1], model_folder,
train_params['input_shape'],
score=train_params.get('eval_val_score'), nms_iou=0.5,
best_weights=False, raw_eval='def')
eval_stats_val_st2, videos = get_full_evaluation(annotation_files[1], preds_filename_val_st2, class_names, full)
print('='*80)
print('mAP@50 | val_bw: {:.2f} | val_st2: {:2f}'.format(eval_stats_val_bw['total'][1]*100, eval_stats_val_st2['total'][1]*100))
print('='*80)
# Pred and eval train
best_weights = eval_stats_val_bw['total'][1] > eval_stats_val_st2['total'][1]
if eval_train:
_, preds_filename_train = prediction_utils.predict_and_store_from_annotations(model_folder,
train_params, annotation_files[0], model_folder,
train_params['input_shape'],
score=train_params.get('eval_train_score'), nms_iou=0.5,
best_weights=best_weights, raw_eval='def')
eval_stats_train, videos = get_full_evaluation(annotation_files[0], preds_filename_train, class_names, full)
else:
eval_stats_train = None
train_diff, train_loss, val_loss = get_train_resume(model_folder)
eval_stats_val = eval_stats_val_bw if best_weights else eval_stats_val_st2
full_resume = get_excel_resume_full(model_folder, train_params,
train_loss, val_loss,
eval_stats_train, eval_stats_val, train_diff,
best_weights)
print('='*80)
print(full_resume)
pyperclip.copy(full_resume)
print('='*80)
if eval_train:
print('mAP@50 | val_bw: {:.4f} | val_st2: {:.4f} | train: {:.4f}'.format(
eval_stats_val_bw['total'][1]*100, eval_stats_val_st2['total'][1]*100, eval_stats_train['total'][1]*100
))
print('R10 | val_bw: {:.4f} | val_st2: {:.4f} | train: {:.4f}'.format(
eval_stats_val_bw['total'][7]*100, eval_stats_val_st2['total'][7]*100, eval_stats_train['total'][7]*100
))
else:
print('mAP@50 | val_bw: {:.4f} | val_st2: {:.4f}'.format(
eval_stats_val_bw['total'][1]*100, eval_stats_val_st2['total'][1]*100
))
print('R10 | val_bw: {:.4f} | val_st2: {:.4f}'.format(
eval_stats_val_bw['total'][7]*100, eval_stats_val_st2['total'][7]*100
))
print('='*80)
# return full_resume
return eval_stats_train, eval_stats_val, videos, class_names
model_path = 'base_models/yolov3-openimages.h5'
classes_path = 'base_models/openimages_classes.txt'
elif model == 'voc':
# path_dataset = '/mnt/hdd/datasets/adl_dataset/ADL_frames/'
input_shape = (416, 416)
# path_dataset = '/mnt/hdd/datasets/VOC/'
# path_annotations = ['./dataset_scripts/voc/annotations_voc_train.txt',
# './dataset_scripts/voc/annotations_voc_val.txt']
## path_annotations = ['/home/asabater/projects/ADL_dataset/annotations_adl_train.txt',
## '/home/asabater/projects/ADL_dataset/annotations_adl_val.txt']
# path_classes = './dataset_scripts/voc/voc_classes.txt'
# anchors_path = 'base_models/yolo_anchors.txt'
model_num = 0
model_folder = train_utils.get_model_path('/mnt/hdd/egocentric_results/',
'voc', model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
# model_path = model_folder + 'weights/trained_weights_stage_1.h5'
classes_path = train_params['path_classes']
anchors_path = train_params['path_anchors']
model_image_size = train_params['input_shape']
path_base = '/mnt/hdd/datasets/VOC/'
elif model == 'adl':
# anchors_path = 'base_models/yolo_anchors.txt'
model_num = 16
model_folder = train_utils.get_model_path('/mnt/hdd/egocentric_results/',
'adl', model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
# model_path = model_folder + 'weights/trained_weights_stage_1.h5'
classes_path = train_params['path_classes']
model_nums, dataset_name, score = {64: 'original', 66: 'v2', 62: 'v3'}, 'adl', 0
#model_nums, dataset_name, score = {56:'v2 old', 52: 'v2', 60: 'v2 | xy: 5, wh: 5, conf_obj: 2.3'}, 'adl', 0
#model_nums, dataset_name, score = {57:'v3 old', 53: 'v3 new'}, 'adl', 0
#model_nums, dataset_name, score = {0: 'cv1_17', 1: 'cv2_18'}, 'kitchen', 0.005
models = {}
preds = {}
for model_num, _ in model_nums.items():
preds_file = '/'.join(video_file.split('/')[:-1]) + '/predictions/' + \
video_file.split('/')[-1][:-4] + \
'_model{}_ms{}'.format(model_num, score) + '.pckl'
model_folder = train_utils.get_model_path('/mnt/hdd/egocentric_results/', dataset_name, model_num)
train_params = json.load(open(model_folder + 'train_params.json', 'r'))
classes_path = train_params['path_classes']
anchors_path = train_params['path_anchors']
model_image_size = train_params['input_shape']
model_path = get_best_weights(model_folder, train_params, score=score)
model = EYOLO(
model_image_size = (416, 416),
model_path = model_path,
anchors_path = anchors_path,
classes_path = classes_path,
score = score,
iou = 0.5,
# gpu_num = 2
td_len = train_params['td_len'],