loc = 1
I = cv.imread(testing_list[1], Clr_flag)
s = np.shape(I)
fscore_tot = []
iou_tot = []
map_tot = []
bboxTP_tot = []
bboxFN_tot = []
bboxFP_tot = []
bboxTP_tot = 0
bboxFN_tot = 0
bboxFP_tot = 0
Bbox = ut.get_bboxes_from_MOTChallenge(gt_file) #Ground truth Bboxes
for loc, filename in enumerate(testing_list):
I = ut.getImg_D(filename,
D=d,
color_space=COLOR_SPACE,
color_channels=COLOR_CHANNELS)
if d == 1:
I = np.squeeze(I, axis=2)
I = np.squeeze(I, axis=2)
frm = ut.frameIdfrom_filename(
testing_list[loc]) # Get the number of the frame from filename
print(frm)
ax2.imshow(std_bg, vmin=0, vmax=255)
else:
ax1.imshow(mu_bg, cmap='gray')
ax2.imshow(std_bg, cmap='gray')
ax1.set_title("Mean background model over {} frames".format(
len(train_frames)))
ax2.set_title("Standard noise backgrond model")
plt.savefig(os.path.join(results_dir, 'mean_std_testing.png'))
# Threshold to create different masks
ths = [2, 2.5, 3, 3.5]
# Get image size of the frames
frame_img = cv.imread(test_frames[1], color_flag)
# Get bounding boxes from ground truth
bboxes_gt = ut.get_bboxes_from_MOTChallenge(gt_file)
# Iterate over testing frames to choose one with bounding boxes
for test_frame in test_frames:
# Get frame ID from frame filename
frm = ut.frameIdfrom_filename(test_frame)
# TODO: VER
# Get mask and list of bounding boxes from the
fore_mask, cbbox = ut.getbboxmask(bboxes_gt, frm, frame_img.shape[:2])
# If there are bounding boxes in the ground truth
if any(cbbox):
frame_img = cv.imread(test_frame, color_flag)
break
# Plot different thresholds
# Directory in the root directory where the results will be saved
# Useful directories
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
TRAIN_DIR = os.path.join('dataset', 'train')
TRAIN_DIR_GT = os.path.join(TRAIN_DIR, 'gt')
OUTPUT_DIR = os.path.join(ROOT_DIR, 'output')
FIGURES_DIR = os.path.join(OUTPUT_DIR, 'figures')
IMG_SHAPE = (1080, 1920)
threshold = 0.5 # IoU Threshold
if __name__ == '__main__':
# List of DataFrames
df = u.get_bboxes_from_MOTChallenge(TRAIN_DIR_GT) # CHECK IF THIS IS THE CORRECT FUCTION!
df_grouped = df.groupby('frame')
vals = list()
# iterate over each group
for group_name, df_group in df_grouped:
frame = df_group['frame'].values[0]
df_gt = df_group[['ymin', 'xmin', 'ymax', 'xmax']].values.tolist()
# Correct order: tly, tlx, bry, brx
frame_vals = [frame]
fscore, iou, map = uBG.compute_metrics_general(df_gt, BBOX!!!