'''
visualizer.py
Visualizer for Detection, Human Pose Estimation, Segmentation, etc
Author: Guanghan Ning
E-mail: guanghan.ning@jd.com
Created on June 19th, 2018
'''
import sys, os
sys.path.insert(0, os.path.abspath("../detect_to_standard/"))
from detection_visualizer import *
sys.path.append(os.path.abspath("../keypoint_to_standard/"))
from keypoint_visualizer import *
draw_threshold = 0.4
def show_all_from_standard_json(json_file_path, classes, joint_pairs, joint_names, img_folder_path = None, output_folder_path = None, flag_track= False):
# Visualizing: Detection + Pose Estimation
dets = read_json_from_file(json_file_path)
for det in dets:
python_data = det
if img_folder_path is None:
img_path = os.path.join(python_data["image"]["folder"], python_data["image"]["name"])
else:
img_path = os.path.join(img_folder_path, python_data["image"]["name"])
print(img_path)
if is_image(img_path): img = cv2.imread(img_path)
candidates = python_data["candidates"]
for candidate in candidates:
bbox = np.array(candidate["det_bbox"]).astype(int)
score = candidate["det_score"]
if score < draw_threshold: continue
'''
# optional: show the bounding boxes
if flag_track is True:
track_id = candidate["track_id"]
img = draw_bbox(img, bbox, score, classes, track_id = track_id)
else:
#img = draw_bbox(img, bbox, score, classes)
img = draw_bbox(img, bbox, score, classes, -1, python_data["image"]["id"][0]) #for lighttrack
'''
pose_keypoints_2d = candidate["pose_keypoints_2d"]
joints = reshape_keypoints_into_joints(pose_keypoints_2d)
if flag_track is True:
track_id = candidate["track_id"]
img = show_poses_from_python_data(img, joints, joint_pairs, joint_names, track_id = track_id)
#img = show_poses_from_python_data(img, joints, joint_pairs, joint_names)
else:
img = show_poses_from_python_data(img, joints, joint_pairs, joint_names)
if output_folder_path is not None:
create_folder(output_folder_path)
img_output_path = os.path.join(output_folder_path, python_data["image"]["name"])
cv2.imwrite(img_output_path, img)
return
def make_video_from_images(img_paths, outvid_path, fps=25, size=None,
is_color=True, format="XVID"):
"""
Create a video from a list of images.
@param outvid output video
@param images list of images to use in the video
@param fps frame per second
@param size size of each frame
@param is_color color
@param format see http://www.fourcc.org/codecs.php
@return see http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_gui/py_video_display/py_video_display.html
The function relies on http://opencv-python-tutroals.readthedocs.org/en/latest/.
By default, the video will have the size of the first image.
It will resize every image to this size before adding them to the video.
"""
from cv2 import VideoWriter, VideoWriter_fourcc, imread, resize
fourcc = VideoWriter_fourcc(*format)
vid = None
for ct, img_path in enumerate(img_paths):
if not os.path.exists(img_path):
raise FileNotFoundError(img_path)
img = imread(img_path)
if img is None:
print(img_path)
continue
if vid is None:
if size is None:
size = img.shape[1], img.shape[0]
vid = VideoWriter(outvid_path, fourcc, float(fps), size, is_color)
if size[0] != img.shape[1] and size[1] != img.shape[0]:
img = resize(img, size)
vid.write(img)
if vid is not None:
vid.release()
return vid
def make_gif_from_images(img_paths, outgif_path):
import imageio
resize_ratio = 4
skip_ratio = 2
with imageio.get_writer(outgif_path, mode='I') as writer:
for img_id, img_path in enumerate(img_paths):
image = imageio.imread(img_path)
image_resize = image[::resize_ratio, ::resize_ratio, :]
# Do sth to make gif file smaller
# 1) change resolution
# 2) change framerate
if img_id % skip_ratio == 0:
writer.append_data(image_resize)
print("Gif made!")
return
