# - Estimation " " with respect to the BBox - ignoring them from the calculation
# Set useful directories
frames_dir = os.path.join(w.ROOT_DIR, 'frames')
results_dir = os.path.join(w.OUTPUT_DIR, 'week2', 'task2', EXP_NAME)
# Ground truth file path
gt_file = os.path.join(w.ROOT_DIR, 'datasets', 'AICity_data', 'train',
'S03', 'c010', 'gt', 'gt.txt')
# Create folders if they don't exist
if not os.path.isdir(results_dir):
os.mkdir(results_dir)
# Get file paths for each of the frames and sort them according
# to the frame number
frame_paths = ut.get_files_from_dir2(frames_dir, ext='.jpg')
frame_paths.sort(key=ut.natural_keys)
# Total number of frames
num_frames = len(frame_paths)
# Flag to show the results based on image dimension
color_flag = cv.IMREAD_GRAYSCALE if DIM <= 1 else cv.IMREAD_COLOR
# Get the the images for training
num_frames_test = int(num_frames * N)
# Separate frames for training and testing
train_frames = frame_paths[:num_frames_test]
test_frames = frame_paths[num_frames_test:]
# For visulization
import matplotlib.pyplot as plt
import matplotlib.patches as patches
"""
task 1
1 Gaussian Background model
Estimating on 25% of the video frame
- Estimation of the background without consideration of the foreground in the gt.txt
- Estimation " " with respect to the BBox - ignoring them from the calculation
'
"""
frames_dir = '../frames'
gt_file = '../datasets/AICity_data/train/S03/c010/gt/gt.txt'
frame_list = ut.get_files_from_dir2(frames_dir,ext = '.jpg')
frame_list.sort(key=ut.natural_keys)
output_dir = '../week2_results/'
output_subdir = 'BG_1G/'
exp_name = 'BG1G_noGT'
if not os.path.isdir(output_dir+output_subdir):
os.mkdir(output_dir+output_subdir)
#frame_list = ut.get_files_from_dir(frames_dir, excl_ext='jpg')
#print frame_list
# training
N =len(frame_list)
d =1
COLOR_SPACE = None #cv.COLOR_BGR2HSV
COLOR_CHANNELS = [] #[0,1]