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Charles Joseph Pierre Beauville 2021-06-27 23:43:29 +02:00
parent f302fd5b86
commit 9fe42480c1
3 changed files with 53 additions and 57 deletions
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2
monoloco/prep/__init__.py
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@ -1,4 +1,4 @@
from .preprocess_kitti import parse_ground_truth, factory_file
from .casr_preprocess import create_dic
from .casr_preprocess_standard import create_dic_std
from .casr_preprocess_standard import create_dic_std

107
monoloco/prep/casr_preprocess.py
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@ -1,36 +1,32 @@
import pickle
import re
import numpy as np
import json
import os
import glob
import datetime
from collections import defaultdict
import numpy as np
import torch
from .. import __version__
from .transforms import flip_inputs, flip_labels, height_augmentation
from ..network.process import preprocess_monoloco
gt_path = '/scratch/izar/beauvill/casr/data/annotations/casr_annotation.pickle'
res_path = '/scratch/izar/beauvill/casr/res_extended/casr*'
def bb_intersection_over_union(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def match_bboxes(bbox_gt, bbox_pred, IOU_THRESH=1):
def match_bboxes(bbox_gt, bbox_pred):
n_true = bbox_gt.shape[0]
n_pred = bbox_pred.shape[0]
MAX_DIST = 1.0
MIN_IOU = 0.0
iou_matrix = np.zeros((n_true, n_pred))
for i in range(n_true):
@ -40,53 +36,54 @@ def match_bboxes(bbox_gt, bbox_pred, IOU_THRESH=1):
return np.argmax(iou_matrix)
def standard_bbox(bbox):
return [bbox[0], bbox[1], bbox[0]+bbox[2], bbox[1]+bbox[3]]
return [bbox[0], bbox[1], bbox[0]+bbox[2], bbox[1]+bbox[3]]
def load_gt(path=gt_path):
return pickle.load(open(path, 'rb'), encoding='latin1')
return pickle.load(open(path, 'rb'), encoding='latin1')
def load_res(path=res_path):
mono = []
for dir in sorted(glob.glob(path), key=lambda x:float(re.findall("(\d+)",x)[0])):
data_list = []
for file in sorted(os.listdir(dir), key=lambda x:float(re.findall("(\d+)",x)[0])):
if 'json' in file:
json_path = os.path.join(dir, file)
json_data = json.load(open(json_path))
json_data['filename'] = json_path
data_list.append(json_data)
mono.append(data_list)
return mono
mono = []
for folder in sorted(glob.glob(path), key=lambda x:float(re.findall(r"(\d+)",x)[0])):
data_list = []
for file in sorted(os.listdir(folder), key=lambda x:float(re.findall(r"(\d+)",x)[0])):
if 'json' in file:
json_path = os.path.join(folder, file)
json_data = json.load(open(json_path))
json_data['filename'] = json_path
data_list.append(json_data)
mono.append(data_list)
return mono
def create_dic(gt=load_gt(), res=load_res()):
dic_jo = {
'train': dict(X=[], Y=[], names=[], kps=[]),
'val': dict(X=[], Y=[], names=[], kps=[]),
'version': __version__,
}
split = ['3', '4']
for i in range(len(res[:])):
for j in range(len(res[i][:])):
folder = gt[i][j]['video_folder']
def create_dic():
gt=load_gt()
res=load_res()
dic_jo = {
'train': dict(X=[], Y=[], names=[], kps=[]),
'val': dict(X=[], Y=[], names=[], kps=[]),
'version': __version__,
}
split = ['3', '4']
for i in range(len(res[:])):
for j in [x for x in range(len(res[i][:])) if 'boxes' in res[i][x]]:
folder = gt[i][j]['video_folder']
phase = 'val'
if folder[7] in split:
phase = 'train'
phase = 'val'
if folder[7] in split:
phase = 'train'
if('boxes' in res[i][j]):
gt_box = gt[i][j]['bbox_gt']
gt_box = gt[i][j]['bbox_gt']
good_idx = match_bboxes(np.array([standard_bbox(gt_box)]), np.array(res[i][j]['boxes'])[:,:4])
good_idx = match_bboxes(np.array([standard_bbox(gt_box)]), np.array(res[i][j]['boxes'])[:,:4])
keypoints = [res[i][j]['uv_kps'][good_idx]]
inp = preprocess_monoloco(keypoints, torch.eye(3)).view(-1).tolist()
dic_jo[phase]['kps'].append(keypoints)
dic_jo[phase]['X'].append(inp)
dic_jo[phase]['Y'].append(gt[i][j]['left_or_right'])
dic_jo[phase]['names'].append(folder+"_frame{}".format(j))
keypoints = [res[i][j]['uv_kps'][good_idx]]
now_time = datetime.datetime.now().strftime("%Y%m%d-%H%M")[2:]
with open("/home/beauvill/joints-casr-right-" + split[0] + split[1] + "-" + now_time + ".json", 'w') as file:
json.dump(dic_jo, file)
return dic_jo
inp = preprocess_monoloco(keypoints, torch.eye(3)).view(-1).tolist()
dic_jo[phase]['kps'].append(keypoints)
dic_jo[phase]['X'].append(inp)
dic_jo[phase]['Y'].append(gt[i][j]['left_or_right'])
dic_jo[phase]['names'].append(folder+"_frame{}".format(j))
now_time = datetime.datetime.now().strftime("%Y%m%d-%H%M")[2:]
with open("/home/beauvill/joints-casr-right-" + split[0] + split[1] + "-" + now_time + ".json", 'w') as file:
json.dump(dic_jo, file)
return dic_jo

1
monoloco/train/hyp_tuning_casr.py
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@ -66,7 +66,6 @@ class HypTuningCasr:
best_acc_val = 20
dic_best = {}
dic_err_best = {}
start = time.time()
cnt = 0
for idx, lr in enumerate(self.lr_list):