def code_main(my_detector, multipeople_classifier, multiperson_tracker, image):
try:
humans = my_detector.detect(image)
count_human = len(humans)
# print('count_human',len(humans))
skeletons, scale_y = my_detector.humans_to_skelsList(humans)
skeletons = remove_skeletons_with_few_joints(skeletons)
dict_id2skeleton = multiperson_tracker.track(
skeletons) # int id -> np.array() skeleton
min_id = min(dict_id2skeleton.keys())
dict_id2label = multipeople_classifier.classify(dict_id2skeleton)
print("prediced label is :", dict_id2label[min_id])
label = dict_id2label[min_id]
my_detector.draw(image, humans) # Draw all skeletons
ith_img = 1
if len(dict_id2skeleton):
for id, label in dict_id2label.items():
skeleton = dict_id2skeleton[id]
skeleton[1::2] = skeleton[1::2] / scale_y
drawActionResult(image, id, skeleton, label)
# image_disp = add_white_region_to_left_of_image(image)
draw_resulf(image, dict_id2label[min_id])
image_disp = image
# multipeople_classifier.get(id='min').draw_scores_onto_image(image_disp)
ret, jpeg = cv2.imencode('.jpg', image_disp)
return count_human, label, jpeg.tobytes()
except:
print('no_human')
ret, jpeg = cv2.imencode('.jpg', image)
return 'none', 'none', jpeg.tobytes()
dict_id2skeleton = {min_id: dict_id2skeleton[min_id]}
dict_id2label = {min_id: img_action_type}
print("Ground_truth label is :", dict_id2label[min_id])
# -- Draw
my_detector.draw(image_disp, humans) # Draw all skeletons
if len(dict_id2skeleton):
# Draw outer box and label for each person
for id, label in dict_id2label.items():
skeleton = dict_id2skeleton[id]
skeleton[1::2] = skeleton[
1::2] / scale_y # scale the y data back to original
# print("Drawing skeleton: ", dict_id2skeleton[id], "with label:", label, ".")
drawActionResult(image_disp, id, skeleton, label)
# Add blank to the left for displaying prediction scores of each class
image_disp = add_white_region_to_left_of_image(image_disp)
# Draw predicting score for only 1 person (not using for)
if DO_INFER_ACTIONS and len(dict_id2skeleton):
multipeople_classifier.get(
id='min').draw_scores_onto_image(image_disp)
# -- Write skeleton.txt and image.png
if SAVE_RESULTANT_SKELETON_TO_TXT_AND_IMAGE:
ids = sorted(dict_id2skeleton.keys())
skel_to_save = [
img_info + dict_id2skeleton[id].tolist() for id in ids
else:
prediced_label = action_type
print("Ground_truth label is :", prediced_label)
# Draw skeleton
if ith_skel == 0:
my_detector.draw(image_disp, humans)
if DO_INFER_ACTIONS:
# Draw score
if DO_INFER_ACTIONS:
classifier.draw_scores_onto_image(image_disp)
# Draw bounding box and action type
drawActionResult(
image_disp,
SkeletonDetector.get_ith_skeleton(
skelsList_no_scale_y, target_idx), prediced_label)
else:
# tracker.reset() # clear the prev
classifier.reset() # clear the deque
# Write result to txt/png
if SAVE_RESULTANT_SKELETON_TO_TXT_AND_IMAGE:
skel_to_save = []
for ith_skel in range(0, len(skelsList)):
skel_to_save.append(img_info +
SkeletonDetector.get_ith_skeleton(
skelsList, ith_skel).tolist())
myio.save_skeletons(