def draw_result_img(img_disp, ith_img, humans, dict_id2skeleton,
skeleton_detector, multiperson_classifier):
''' Draw skeletons, labels, and prediction scores onto image for display '''
# Resize to a proper size for display
r, c = img_disp.shape[0:2]
desired_cols = int(1.0 * c * (img_disp_desired_rows / r))
img_disp = cv2.resize(img_disp,
dsize=(desired_cols, img_disp_desired_rows))
# Draw all people's skeleton
skeleton_detector.draw(img_disp, humans)
# Draw bounding box and label of each person
if len(dict_id2skeleton):
for id, label in dict_id2label.items():
skeleton = dict_id2skeleton[id]
# scale the y data back to original
skeleton[1::2] = skeleton[1::2] / scale_h
# print("Drawing skeleton: ", dict_id2skeleton[id], "with label:", label, ".")
lib_plot.draw_action_result(img_disp, id, skeleton, label)
# Add blank to the left for displaying prediction scores of each class
#img_disp = lib_plot.add_white_region_to_left_of_image(img_disp)
##################################################################################################################################
#cv2.putText(img_disp, "Frame:" + str(ith_img),
# (20, 20), fontScale=1.5, fontFace=cv2.FONT_HERSHEY_PLAIN,
# color=(0, 0, 0), thickness=2)
##############################################################################################################
# Draw predicting score for only 1 person
if len(dict_id2skeleton):
classifier_of_a_person = multiperson_classifier.get_classifier(
id='min')
classifier_of_a_person.draw_scores_onto_image(img_disp)
return img_disp
def draw_result_img(img_disp, ith_img, humans, dict_id2skeleton,
skeleton_detector, multiperson_classifier,
img_disp_desired_rows, dict_id2label, scale_h):
'''
Draw skeletons, labels, and prediction scores onto image for display
img_disp => copy of the input image
'''
# Resize to a proper size for display
r, c = img_disp.shape[
0:2] # find the size of image, height 480 and width 640
desired_cols = int(1.0 * c * (img_disp_desired_rows / r))
# resize to 640*480 => unchange
img_disp = cv2.resize(img_disp,
dsize=(desired_cols, img_disp_desired_rows))
# Draw all people's skeleton
skeleton_detector.draw(img_disp, humans)
# Draw bounding box and label of each person
if len(dict_id2skeleton):
for id, label in dict_id2label.items():
skeleton = dict_id2skeleton[id]
# scale the y data back to original
skeleton[
1::2] = skeleton[1::2] / scale_h # y 在擷取特徵的時候有乘scale_h，要除回來
lib_plot.draw_action_result(img_disp, id, skeleton, label)
# add text "fps"
cv2.putText(img_disp,
"Frame:" + str(ith_img), (10, 25),
fontScale=1,
fontFace=cv2.FONT_HERSHEY_PLAIN,
color=(0, 0, 0),
thickness=2)
# Draw predicting score for only 1 person
# 取最小的id
if len(dict_id2skeleton):
classifier_of_a_person = multiperson_classifier.get_classifier(
id='min')
classifier_of_a_person.draw_scores_onto_image(img_disp)
return img_disp