def main_sketch_run(INPUT_FRAMES, RUN_NAME, SETTINGS):
video_file_root_folder = str(Path(INPUT_FRAMES).parents[1])
output_frames_folder = video_file_root_folder + "/output/" + RUN_NAME + "/frames/"
output_measurement_viz = video_file_root_folder + "/output/" + RUN_NAME + "/graphs"
output_annotation = video_file_root_folder + "/output/" + RUN_NAME + "/annot"
output_savedLastExp = video_file_root_folder + "/output/" + RUN_NAME + "/_lastExpiment.npy"
mask_folder = video_file_root_folder + "/temporary/"+RUN_NAME+"/masks/"
mask_crop_folder = video_file_root_folder + "/temporary/"+RUN_NAME+"/mask_crops/" # useless, but maybe for debug later
crops_folder = video_file_root_folder + "/temporary/"+RUN_NAME+"/crops/" # also useless, but maybe for debug later
for folder in [output_frames_folder]:
if not os.path.exists(folder):
os.makedirs(folder)
attention_model = SETTINGS["attention"]
attention_spread_frames = SETTINGS["att_frame_spread"]
# Frames to crops
files = sorted(os.listdir(INPUT_FRAMES))
#print("files",len(files), files[0:10])
files = [path for path in files if is_non_zero_file(INPUT_FRAMES+path)]
#print("files", len(files), files[0:10])
frame_files = fnmatch.filter(files, '*.jpg')
annotation_files = fnmatch.filter(files, '*.xml')
print("jpgs:",frame_files[0:2],"...","xmls:",annotation_files[0:2],"...")
start_frame = SETTINGS["startframe"]
end_frame = SETTINGS["endframe"]
if end_frame is not -1:
frame_files = frame_files[start_frame:end_frame]
else:
frame_files = frame_files[start_frame:]
allowed_number_of_boxes = SETTINGS["allowed_number_of_boxes"]
# fill these in code, later print them for statistics
number_of_crops_attention = []
number_of_crops_evaluation = []
if not SETTINGS["reuse_last_experiment"]:
print("################## Mask generation ##################")
summed_mask_croping_time = []
if attention_model:
print("##", len(frame_files), "of frames")
# 1 generate crops from full images
mask_crops_per_frames = []
scales_per_frames = []
mask_crops_number_per_frames = []
for frame_i in range(0, len(frame_files)):
start = timer()
frame_path = INPUT_FRAMES + frame_files[frame_i]
# working with many large files - relatively slow
mask_crops, scale_full_img, attention_crop_TMP_SIZE_FOR_MODEL = mask_from_one_frame(frame_path,
SETTINGS, mask_crop_folder) ### <<< mask_crops
mask_crops_per_frames.append(mask_crops)
mask_crops_number_per_frames.append(len(mask_crops))
scales_per_frames.append(scale_full_img)
end = timer()
time = (end - start)
summed_mask_croping_time.append(time)
print("")
number_of_crops_attention = mask_crops_number_per_frames
# 2 eval these calculate
masks_evaluation_times, masks_additional_times, bboxes_per_frames = run_yolo(mask_crops_number_per_frames,
mask_crops_per_frames, attention_crop_TMP_SIZE_FOR_MODEL, INPUT_FRAMES, frame_files,
resize_frames=scales_per_frames, allowed_number_of_boxes=allowed_number_of_boxes,
VERBOSE=0, anchors_txt=SETTINGS["anchorfile"])
# 3 make mask images accordingly
save_masks = SETTINGS["debug_save_masks"]
range_of_masks = []
if save_masks is "one":
range_of_masks = [0]
elif save_masks is "all":
range_of_masks = range(0, len(frame_files))
for i in range_of_masks:
# for i in range(0,len(frame_files)):
print(output_measurement_viz + frame_files[i])
tmp_mask_just_to_save_it_for_debug = mask_from_evaluated_bboxes(INPUT_FRAMES + frame_files[i],
output_measurement_viz + frame_files[i], bboxes_per_frames[i], scales_per_frames[i], 0) # SETTINGS["extend_mask_by"]
print("################## Cropping frames : extracting crops from images ##################")
print("##", len(frame_files), "of frames")
crop_per_frames = []
crop_number_per_frames = []
summed_croping_time = []
save_one_crop_vis = True
for frame_i in range(0, len(frame_files)):
start = timer()
frame_path = INPUT_FRAMES + frame_files[frame_i]
if attention_model:
if attention_spread_frames == 0:
bboxes = bboxes_per_frames[frame_i]
# print(len(bboxes), bboxes)
else:
from_frame = max([frame_i - attention_spread_frames, 0])
to_frame = min([frame_i + attention_spread_frames, len(frame_files)]) + 1
bboxes = [item for sublist in bboxes_per_frames[from_frame:to_frame] for item in sublist]
# print(from_frame,"to",to_frame-1,len(bboxes), bboxes)
scale = scales_per_frames[frame_i]
img = Image.open(frame_path)
mask = bboxes_to_mask(bboxes, img.size, scale, SETTINGS["extend_mask_by"])
mask_over = 0.1 # SETTINGS["over"]
horizontal_splits = SETTINGS["horizontal_splits"]
overlap_px = SETTINGS["overlap_px"]
crops, crop_TMP = crop_from_one_frame_WITH_MASK_in_mem(img, mask, frame_path, crops_folder,
horizontal_splits, overlap_px, mask_over, show=False, save_crops=False,
save_visualization=save_one_crop_vis, viz_path=output_measurement_viz)
else:
horizontal_splits = SETTINGS["horizontal_splits"]
overlap_px = SETTINGS["overlap_px"]
crops, crop_TMP = crop_from_one_frame(frame_path, crops_folder, horizontal_splits, overlap_px,
show=False, save_visualization=save_one_crop_vis, save_crops=False, viz_path=output_measurement_viz)
crop_per_frames.append(crops)
crop_number_per_frames.append(len(crops))
save_one_crop_vis = False
end = timer()
time = (end - start)
summed_croping_time.append(time)
crop_TMP_SIZE_FOR_MODEL = crop_TMP
horizontal_splits = SETTINGS["horizontal_splits"]
overlap_px = SETTINGS["overlap_px"]
max_number_of_crops_per_frame = get_number_of_crops_from_frame(INPUT_FRAMES + frame_files[0], horizontal_splits,
overlap_px)
# print("max_number_of_crops_per_frame",max_number_of_crops_per_frame)
# tmp_crops,_ = crop_from_one_frame(INPUT_FRAMES + frame_files[0], crops_folder, horizontal_splits,overlap_px,
# show=False, save_visualization=False, save_crops=False,viz_path='')
# max_number_of_crops_per_frame = len(tmp_crops)
number_of_crops_evaluation = crop_number_per_frames
if not SETTINGS["debug_just_count_hist"]:
# Run YOLO on crops
print("")
print("################## Running Model ##################")
print("Crop size = ", crop_TMP_SIZE_FOR_MODEL)
pureEval_times, ioPlusEval_times, bboxes_per_frames = run_yolo(crop_number_per_frames, crop_per_frames,
crop_TMP_SIZE_FOR_MODEL, INPUT_FRAMES,
frame_files, anchors_txt=SETTINGS["anchorfile"],
allowed_number_of_boxes=allowed_number_of_boxes)
num_frames = len(crop_number_per_frames)
num_crops = len(crop_per_frames[0])
print("################## Save Graphs ##################")
print(len(pureEval_times), pureEval_times[0:3])
# evaluation_times[0] = evaluation_times[1] # ignore first large value
# masks_evaluation_times[0] = masks_evaluation_times[1] # ignore first large value
visualize_time_measurements([pureEval_times], ["Evaluation"], "Time measurements all frames", show=False,
save=True, save_path=output_measurement_viz + '_1.png', y_min=0.0, y_max=0.5)
visualize_time_measurements([pureEval_times], ["Evaluation"], "Time measurements all frames", show=False,
save=True, save_path=output_measurement_viz + '_1.png', y_min=0.0, y_max=0.0)
last = 0
summed_frame_measurements = []
for f in range(0, num_frames):
till = crop_number_per_frames[f]
sub = pureEval_times[last:last + till]
summed_frame_measurements.append(sum(sub))
# print(last,till,sum(sub))
last = till
if attention_model:
last = 0
summed_mask_measurements = []
for f in range(0, num_frames):
till = mask_crops_number_per_frames[f]
sub = masks_evaluation_times[last:last + till]
summed_mask_measurements.append(sum(sub))
# print(last,till,sum(sub))
last = till
avg_time_crop = np.mean(pureEval_times[1:])
max_time_per_frame_estimate = max_number_of_crops_per_frame * avg_time_crop
estimated_max_time_per_frame = [max_time_per_frame_estimate] * num_frames
if attention_model:
arrs = [summed_frame_measurements, summed_mask_measurements, summed_croping_time, summed_mask_croping_time,
ioPlusEval_times, masks_additional_times, estimated_max_time_per_frame]
names = ['image eval', 'mask eval', 'cropping image', 'cropping mask', 'image eval+io', 'mask eval+io',
'estimated max']
else:
arrs = [summed_frame_measurements, summed_croping_time, ioPlusEval_times]
names = ['image eval', 'cropping image', 'image eval+io']
visualize_time_measurements(arrs, names, "Time measurements per frame", xlabel='frame #',
show=False, save=True, save_path=output_measurement_viz + '_3.png')
## save simpler graphs - versions b and c
if attention_model:
arrs = [summed_frame_measurements, summed_mask_measurements,
ioPlusEval_times, masks_additional_times, estimated_max_time_per_frame]
names = ['image eval', 'mask eval', 'image eval+io', 'mask eval+io',
'estimated max']
visualize_time_measurements(arrs, names, "Time measurements per frame", xlabel='frame #',
show=False, save=True, save_path=output_measurement_viz + '_3b.png')
arrs = [summed_frame_measurements, summed_mask_measurements, estimated_max_time_per_frame]
names = ['image eval', 'mask eval', 'estimated max']
visualize_time_measurements(arrs, names, "Time measurements per frame", xlabel='frame #',
show=False, save=True, save_path=output_measurement_viz + '_3c.png')
# save settings
avg_time_frame = np.mean(summed_frame_measurements[1:])
strings = [RUN_NAME + " " + str(SETTINGS), INPUT_FRAMES,
str(num_crops) + " crops per frame * " + str(num_frames) + " frames",
"Time:" + str(avg_time_crop) + " avg per crop, " + str(avg_time_frame) + " avg per frame.",
"Crop size in px was:" + str(crop_TMP_SIZE_FOR_MODEL) + "px."]
save_string_to_file(strings, output_measurement_viz + '_settings.txt')
print("################## Saving Last Experiment info ##################")
print("bboxes_per_frames array", len(bboxes_per_frames[0]))
print("crop_per_frames array", len(crop_per_frames[0]))
print("crop_TMP_SIZE_FOR_MODEL", crop_TMP_SIZE_FOR_MODEL)
dict = {}
dict["bboxes_per_frames"] = bboxes_per_frames
dict["crop_per_frames"] = crop_per_frames
dict["crop_TMP_SIZE_FOR_MODEL"] = crop_TMP_SIZE_FOR_MODEL
saveDict(dict,output_savedLastExp)
#saveDict, loadDict
print("################## Save #Crops Histogram data ##################")
reasonable_i = min(5,len(number_of_crops_attention))
print("Crops per frames:")
print("number_of_crops_attention:", number_of_crops_attention[0:reasonable_i])
print("number_of_crops_evaluation:", number_of_crops_evaluation[0:reasonable_i])
print("max crops out of max possible crops:", max(number_of_crops_evaluation), "out of", max_number_of_crops_per_frame)
strings = [RUN_NAME+"_attention;"+";".join(str(x) for x in number_of_crops_attention),
RUN_NAME+"_evaluation;"+";".join(str(x) for x in number_of_crops_evaluation),
RUN_NAME+"_max_evaluation;"+str(max_number_of_crops_per_frame)]
save_string_to_file(strings, output_measurement_viz + '_'+RUN_NAME+'_histogram.csv')
# join with: cat *.csv >> output.csv
visualize_as_histogram([number_of_crops_attention, number_of_crops_evaluation], ["Attention model number of crops", "Evaluation model number of crops"], "Histogram of active crops", xlabel='number of crops active',
show=False, save=True, save_path=output_measurement_viz +'_'+RUN_NAME + '_Hist.png')
else: # reuse_last_experiment is True
print("#!!!!!!!!!!!!!!# WARNING, reusing last experiment #!!!!!!!!!!!!!!#")
print("################## Loading Last Experiment info ##################")
dict = loadDict(output_savedLastExp)
bboxes_per_frames = dict["bboxes_per_frames"]
crop_per_frames = dict["crop_per_frames"]
crop_TMP_SIZE_FOR_MODEL = dict["crop_TMP_SIZE_FOR_MODEL"]
print("bboxes_per_frames array", len(bboxes_per_frames[0]))
print("crop_per_frames array", len(crop_per_frames[0]))
print("crop_TMP_SIZE_FOR_MODEL", crop_TMP_SIZE_FOR_MODEL)
if not SETTINGS["debug_just_count_hist"]:
print("################## Annotating frames ##################")
iou_threshold = 0.5 # towards 0.01 its more drastic and deletes more bboxes which are overlapped
limit_prob_lowest = 0 #0.70 # inside we limited for 0.3
print_first = True
annotations_names_saved = []
annotations_lines_saved = []
import tensorflow as tf
sess = tf.Session()
colors = annotate_prepare()
for frame_i in range(0,len(frame_files)):
test_bboxes = bboxes_per_frames[frame_i]
from_number = len(test_bboxes)
arrays = []
scores = []
for j in range(0,len(test_bboxes)):
if test_bboxes[j][0] == 'person':
score = test_bboxes[j][2]
if score > limit_prob_lowest:
arrays.append(list(test_bboxes[j][1]))
scores.append(score)
arrays = np.array(arrays)
if len(arrays) == 0:
# no bboxes found in there, still we should copy the frame img
copyfile(INPUT_FRAMES + frame_files[frame_i], output_frames_folder + frame_files[frame_i])
continue
person_id = 0
DEBUG_TURN_OFF_NMS = False
if not DEBUG_TURN_OFF_NMS:
"""
nms_arrays = py_cpu_nms(arrays, iou_threshold)
reduced_bboxes_1 = []
for j in range(0,len(nms_arrays)):
a = ['person',nms_arrays[j],0.0,person_id]
reduced_bboxes_1.append(a)
"""
nms_arrays, scores = non_max_suppression_tf(sess, arrays,scores,allowed_number_of_boxes,iou_threshold)
reduced_bboxes_2 = []
for j in range(0,len(nms_arrays)):
a = ['person',nms_arrays[j],scores[j],person_id]
reduced_bboxes_2.append(a)
test_bboxes = reduced_bboxes_2
print("in frame", frame_i, "reduced from", from_number, "to", len(test_bboxes), "bounding boxes with NMS.")
if SETTINGS["postprocess_merge_splitline_bboxes"]:
replace_test_bboxes = postprocess_bboxes_by_splitlines(crop_per_frames[frame_i], test_bboxes, overlap_px_h=SETTINGS["overlap_px"], DEBUG_POSTPROCESS_COLOR=SETTINGS["debug_color_postprocessed_bboxes"])
#test_bboxes += replace_test_bboxes
test_bboxes = replace_test_bboxes
if print_first:
print("Annotating with bboxes of len: ", len(test_bboxes) ,"files in:", INPUT_FRAMES + frame_files[frame_i], ", out:", output_frames_folder + frame_files[frame_i])
print_first = False
img = annotate_image_with_bounding_boxes(INPUT_FRAMES + frame_files[frame_i], output_frames_folder + frame_files[frame_i], test_bboxes, colors,
draw_text=False, save=True, show=False, thickness=SETTINGS["thickness"])
img_size = img.size
if SETTINGS["annotate_frames_with_gt"]:
annotation_name = frame_files[frame_i][:-4]
annotation_path = annotation_name + ".xml"
if annotation_path in annotation_files:
# we have ground truth for this file, we would like to save the predicted annotations
# <image identifier> <confidence> <left> <top> <right> <bottom>
for bbox in test_bboxes:
predicted_class = bbox[0]
if predicted_class is 'crop':
continue
box = bbox[1]
score = bbox[2]
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(img_size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(img_size[0], np.floor(right + 0.5).astype('int32'))
line = str(annotation_name)+" "+str(score)+" "+str(left)+" "+str(top)+" "+str(right)+" "+str(bottom)
annotations_lines_saved.append(line)
annotations_names_saved.append(str(annotation_name))
if SETTINGS["annotate_frames_with_gt"]:
print(len(annotations_lines_saved), annotations_lines_saved[0:3])
with open(output_annotation+'names.txt', 'w') as the_file:
for l in annotations_names_saved:
the_file.write(l+'\n')
with open(output_annotation+'bboxes.txt', 'w') as the_file:
for l in annotations_lines_saved:
the_file.write(l+'\n')
sess.close()
print("################## Cleanup ##################")
keep_temporary = True
if not keep_temporary:
import shutil
temp_dir_del = video_file_root_folder + "/temporary" + RUN_NAME
if os.path.exists(temp_dir_del):
shutil.rmtree(temp_dir_del)
if SETTINGS["debug_just_count_hist"]:
print("DEBUG debug_just_count_hist WAS USED")
if SETTINGS["reuse_last_experiment"]:
print("DEBUG reuse_last_experiment WAS USED")
def main_sketch_run(INPUT_FRAMES, RUN_NAME, SETTINGS):
# 目录路径
video_file_root_folder = str(Path(INPUT_FRAMES).parents[0])
output_frames_folder = video_file_root_folder + "/output/" + RUN_NAME + "/frames/"
# 形成路径数组
folderlist = [output_frames_folder]
# 分别建目录
for folder in folderlist:
if not os.path.exists(folder):
os.makedirs(folder)
# 是否开启注意力评估阶段
attention_model = SETTINGS["attention"]
# 该参数与相邻帧的处理好像有关
attention_spread_frames = SETTINGS["att_frame_spread"]
# 读每个ｆｒａｍｅ
files = sorted(os.listdir(INPUT_FRAMES))
# print("files",len(files), files[0:10])
files = [path for path in files if is_non_zero_file(INPUT_FRAMES + path)]
# print("files", len(files), files[0:10])
frame_files = fnmatch.filter(files, '*.jpg')
print("jpgs:", frame_files[0:2], "...")
# 本轮测试所用ｆｒａｍｅ范围
start_frame = SETTINGS["startframe"]
end_frame = SETTINGS["endframe"]
if end_frame is not -1:
frame_files = frame_files[start_frame:end_frame]
else:
frame_files = frame_files[start_frame:]
# 一帧中允许的检测出的最多的ｂｏｘ数目
allowed_number_of_boxes = SETTINGS["allowed_number_of_boxes"]
# 注意力评估阶段：放缩粗检测，为最终评估提供Ｍａｓｋ，用于激活相应的ｃｒｏｐｓ。
print("################## Mask generation ##############")
crop_per_frames = []
crop_number_per_frames = []
if attention_model:
print("##", len(frame_files), "of frames")
# 生成粗检测所用的分割ｃｒｏｐｓ
# 根据 split、输入图像分辨率运算，切割成若干块，之后把每块缩放成608*608再yolo识别
# 1 generate crops from full images
mask_crops_per_frames = []
scales_per_frames = []
mask_crops_number_per_frames = []
for frame_i in range(0, len(frame_files)):
frame_path = INPUT_FRAMES + frame_files[frame_i]
# working with many large files - relatively slow
mask_crops, scale_full_img, attention_crop_TMP_SIZE_FOR_MODEL = mask_from_one_frame(
frame_path, SETTINGS) ### <<< mask_crops
mask_crops_per_frames.append(mask_crops)
mask_crops_number_per_frames.append(len(mask_crops))
scales_per_frames.append(scale_full_img)
# 2 eval these calculate
# 粗检测
bboxes_per_frames = run_yolo(
mask_crops_number_per_frames,
mask_crops_per_frames,
attention_crop_TMP_SIZE_FOR_MODEL,
INPUT_FRAMES,
frame_files,
resize_frames=scales_per_frames,
allowed_number_of_boxes=allowed_number_of_boxes,
VERBOSE=0)
# 根据粗检测结果激活下一轮切割对应ｃｒｏｐｓ
print(
"################## Cropping frames : extracting crops from images ##################"
)
print("##", len(frame_files), "of frames")
summed_croping_time = []
save_one_crop_vis = True
crop_per_frames = []
crop_number_per_frames = []
for frame_i in range(0, len(frame_files)):
start = timer()
frame_path = INPUT_FRAMES + frame_files[frame_i]
if attention_model:
if attention_spread_frames == 0:
bboxes = bboxes_per_frames[frame_i]
# print(len(bboxes), bboxes)
else:
from_frame = max([frame_i - attention_spread_frames, 0])
to_frame = min(
[frame_i + attention_spread_frames,
len(frame_files)]) + 1
bboxes = [
item for sublist in bboxes_per_frames[from_frame:to_frame]
for item in sublist
]
# print(from_frame,"to",to_frame-1,len(bboxes), bboxes)
scale = scales_per_frames[frame_i]
img = Image.open(frame_path)
mask = bboxes_to_mask(bboxes, img.size, scale,
SETTINGS["extend_mask_by"])
mask_over = 0.1 # SETTINGS["over"]
horizontal_splits = SETTINGS["horizontal_splits"]
overlap_px = SETTINGS["overlap_px"]
crops, crop_TMP = crop_from_one_frame_WITH_MASK_in_mem(
img,
mask,
frame_path,
horizontal_splits,
overlap_px,
mask_over,
show=False,
save_crops=False,
save_visualization=save_one_crop_vis,
)
else:
horizontal_splits = SETTINGS["horizontal_splits"]
overlap_px = SETTINGS["overlap_px"]
crops, crop_TMP = crop_from_one_frame(
frame_path,
horizontal_splits,
overlap_px,
show=False,
save_visualization=save_one_crop_vis,
save_crops=False)
crop_per_frames.append(crops)
crop_number_per_frames.append(len(crops))
save_one_crop_vis = False
end = timer()
time = (end - start)
summed_croping_time.append(time)
crop_TMP_SIZE_FOR_MODEL = crop_TMP
# Run YOLO on crops
print("")
print("################## Running Model ##################")
bboxes_per_frames = run_yolo(
crop_number_per_frames,
crop_per_frames,
crop_TMP_SIZE_FOR_MODEL,
INPUT_FRAMES,
frame_files,
anchors_txt=SETTINGS["anchorfile"],
allowed_number_of_boxes=allowed_number_of_boxes)
iou_threshold = 0.5 # towards 0.01 its more drastic and deletes more bboxes which are overlapped
limit_prob_lowest = 0 # 0.70 # inside we limited for 0.3
sess = tf.Session()
colors = annotate_prepare()
#
for frame_i in range(0, len(frame_files)):
test_bboxes = bboxes_per_frames[frame_i]
from_number = len(test_bboxes)
arrays = []
scores = []
for j in range(0, len(test_bboxes)):
if test_bboxes[j][0] == 'person':
score = test_bboxes[j][2]
if score > limit_prob_lowest:
arrays.append(list(test_bboxes[j][1]))
scores.append(score)
print(arrays)
arrays = np.array(arrays)
if len(arrays) == 0:
# no bboxes found in there, still we should copy the frame img
copyfile(INPUT_FRAMES + frame_files[frame_i],
output_frames_folder + frame_files[frame_i])
continue
person_id = 0
# test
DEBUG_TURN_OFF_NMS = False
if not DEBUG_TURN_OFF_NMS:
"""
nms_arrays = py_cpu_nms(arrays, iou_threshold)
reduced_bboxes_1 = []
for j in range(0,len(nms_arrays)):
a = ['person',nms_arrays[j],0.0,person_id]
reduced_bboxes_1.append(a)
"""
nms_arrays, scores = non_max_suppression_tf(
sess, arrays, scores, allowed_number_of_boxes, iou_threshold)
reduced_bboxes_2 = []
for j in range(0, len(nms_arrays)):
a = ['person', nms_arrays[j], scores[j], person_id]
reduced_bboxes_2.append(a)
test_bboxes = reduced_bboxes_2
print("in frame", frame_i, "reduced from", from_number, "to",
len(test_bboxes), "bounding boxes with NMS.")
if SETTINGS["postprocess_merge_splitline_bboxes"]:
replace_test_bboxes = postprocess_bboxes_by_splitlines(
crop_per_frames[frame_i],
test_bboxes,
overlap_px_h=SETTINGS["overlap_px"],
DEBUG_POSTPROCESS_COLOR=SETTINGS[
"debug_color_postprocessed_bboxes"])
# test_bboxes += replace_test_bboxes
test_bboxes = replace_test_bboxes
annotate_image_with_bounding_boxes(INPUT_FRAMES + frame_files[frame_i],
output_frames_folder +
frame_files[frame_i],
test_bboxes,
colors,
draw_text=False,
save=True,
show=False,
thickness=SETTINGS["thickness"])
sess.close()