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demo.py
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import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
from pathlib import Path
import shutil
import torch
from torch.backends import cuda, cudnn
from callbacks.custom import get_ckpt_callback, get_viz_callback
cuda.matmul.allow_tf32 = True
cudnn.allow_tf32 = True
torch.multiprocessing.set_sharing_strategy('file_system')
from loggers.utils import get_wandb_logger, get_ckpt_path
import hydra
from omegaconf import DictConfig, OmegaConf
import pytorch_lightning as pl
from pytorch_lightning.loggers import CSVLogger
from pytorch_lightning.callbacks import ModelSummary
from config.modifier import dynamically_modify_train_config
from modules.utils.fetch import fetch_data_module, fetch_model_module
import h5py
import hdf5plugin
import ipdb
import numpy as np
import cv2
from tqdm import tqdm
def sort_key(filename):
return int(filename.split('.')[0])
@hydra.main(config_path='config', config_name='val', version_base='1.2')
def main(config: DictConfig):
dynamically_modify_train_config(config)
# Just to check whether config can be resolved
OmegaConf.to_container(config, resolve=True, throw_on_missing=True)
print('------ Configuration ------')
print(OmegaConf.to_yaml(config))
print('---------------------------')
gpus = config.hardware.gpus
assert isinstance(gpus, int), 'no more than 1 GPU supported'
gpus = 'cuda:0'
ckpt_path = Path(config.checkpoint)
module = fetch_model_module(config=config)
module = module.load_from_checkpoint(str(ckpt_path), **{'full_config': config})
module.set_model_to_gpus(gpus)
module = module.eval()
mode = 'pre' # ['gt', 'pre']
show_mode = 'mixed' # ['event', 'rgb', 'mixed']
h5_file = '/data2/zht/fusion_detection/PKU-H5-Process/freq_1_1/test/001_test_low_light'
ev_file = h5_file + '/event_representations_v2/stacked_histogram_dt=50_nbins=10/event_representations.h5'
rgb_file = h5_file + '/labels_v2/images.h5'
labels_gt = h5_file + '/labels_v2/labels.npz'
labels = np.load(labels_gt)['labels']
frame_to_labels = np.load(labels_gt)['objframe_idx_2_label_idx']
frame_to_ev = np.load(h5_file + '/event_representations_v2/stacked_histogram_dt=50_nbins=10/objframe_idx_2_repr_idx.npy')
if mode == 'pre':
images_out_put_dir = 'predictions/images'
video_out_put_dir = 'predictions/video'
elif mode == 'gt':
images_out_put_dir = 'pre_gt/images'
video_out_put_dir = 'pre_gt/video'
if not os.path.exists(images_out_put_dir):
os.makedirs(images_out_put_dir)
else:
shutil.rmtree(images_out_put_dir)
os.makedirs(images_out_put_dir)
if not os.path.exists(video_out_put_dir):
os.makedirs(video_out_put_dir)
else:
shutil.rmtree(video_out_put_dir)
os.makedirs(video_out_put_dir)
ev_file = h5py.File(ev_file)
rgb_file = h5py.File(rgb_file)
event_frames = ev_file['data']
rgb_frames = rgb_file['data'][1:]
bbox_color = (0, 255, 0)
# bbox_color = (255, 255, 255)
unknown_color = (0, 255, 255)
pre_state = None
for frame_index in tqdm(range(event_frames.shape[0])):
# single_frame_shown = (event_frames[frame_index].sum(axis=0) * 60).astype(np.uint8)
# single_frame_shown = cv2.cvtColor(single_frame_shown, cv2.COLOR_GRAY2BGR)
ev_pr = event_frames[frame_index]
rgb_pr = rgb_frames[frame_index]
num_bins = int(ev_pr.shape[0] / 2)
height = int(ev_pr.shape[1])
width = int(ev_pr.shape[2])
ev_pr = np.transpose(ev_pr, (1, 2, 0))
frame = np.zeros((height, width, 3), dtype=np.uint8)
for i in range(num_bins):
pos_image = (ev_pr[:, :, i + num_bins]).astype(np.uint8)
neg_image = (ev_pr[:, :, i]).astype(np.uint8)
pos_image = cv2.equalizeHist(pos_image)
neg_image = cv2.equalizeHist(neg_image)
image = np.concatenate((neg_image[..., None], np.zeros((height, width, 1), dtype=np.uint8), pos_image[..., None]), axis=-1)
frame = np.add(frame, image)
if show_mode == 'event':
single_frame_shown = frame * 255.0
elif show_mode == 'rgb':
single_frame_shown = rgb_pr
elif show_mode == 'mixed':
rgb_pr_show = rgb_pr.copy() # .astype(float) * 2.0
for i in range(num_bins):
pos_image = (ev_pr[:, :, i + num_bins]).astype(np.uint8)
neg_image = (ev_pr[:, :, i]).astype(np.uint8)
pos_image = cv2.equalizeHist(pos_image)
neg_image = cv2.equalizeHist(neg_image)
ev_image = np.concatenate((neg_image[..., None], np.zeros((height, width, 1), dtype=np.uint8), pos_image[..., None]), axis=-1)
rgb_pr_show = np.add(rgb_pr_show, ev_image*30)
single_frame_shown = rgb_pr_show
event_frame = torch.tensor(event_frames[frame_index]).unsqueeze(0)
rgb_frame = torch.tensor(rgb_pr).permute(2,0,1).unsqueeze(0)
event_frame = event_frame.to(gpus)
rgb_frame = rgb_frame.to(gpus)
width = event_frame.shape[3]
height = event_frame.shape[2]
if mode == 'gt':
if frame_index in frame_to_ev:
rgb_frame_index = int(np.where(frame_to_ev == frame_index)[0])
# print('rgb_frame_index{}, len(frame_to_ev){}'.format(rgb_frame_index, len(frame_to_ev)))
if rgb_frame_index+1 < len(frame_to_ev):
results = labels[frame_to_labels[rgb_frame_index]:frame_to_labels[rgb_frame_index+1]]
else:
results = labels[frame_to_labels[rgb_frame_index]:]
else:
results = []
elif mode == 'pre':
with torch.inference_mode():
results, pre_state = module.forward(event_frame, rgb_frame, pre_state)
results = results[0]
if results is None:
results = []
if len(results) > 0:
for each_bbox in results:
if mode == 'gt':
x1, y1 = list(each_bbox)[1:3]
w, h = list(each_bbox)[3:5]
x2, y2 = x1+w, y1+h
confidence = 1.0
label_id = int(list(each_bbox)[5])
if label_id == 0 or label_id == 1 or label_id == 2 or label_id == 3 or label_id == 4:
color = bbox_color
else:
color = unknown_color
elif mode == 'pre':
x1, y1, x2, y2 = list(each_bbox)[0:4]
confidence = round(float(list(each_bbox)[4]), 2)
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
abs_ =int(abs(y1-y2) * abs(x1-x2))
if abs(y1-y2) * abs(x1-x2) < 200:
continue
thickness = 2
if mode == 'pre':
cv2.rectangle(single_frame_shown, (x1, y1), (x2, y2), bbox_color, thickness)
cv2.putText(single_frame_shown, str(confidence), (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, bbox_color, thickness)
elif mode == 'gt':
cv2.rectangle(single_frame_shown, (x1, y1), (x2, y2), color, thickness)
#cv2.putText(single_frame_shown, str(confidence), (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness)
output_file_path = images_out_put_dir + '/{}.png'.format(frame_index)
cv2.imwrite(output_file_path, single_frame_shown)
images = [img for img in os.listdir(images_out_put_dir) if img.endswith(".png")]
images.sort(key=sort_key)
video_name = video_out_put_dir + '/output_video.avi'
fps = 60
frame_size = (width, height)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
video_writer = cv2.VideoWriter(video_name, fourcc, fps, frame_size)
for image in images:
image_path = os.path.join(images_out_put_dir, image)
frame = cv2.imread(image_path)
video_writer.write(frame)
video_writer.release()
print('Save the video in {}'.format(video_name))
if __name__ == '__main__':
main()