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visualizer.py
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visualizer.py
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import cv2
import torch
import numpy as np
from tqdm import tqdm
from PIL import Image, ImageFont, ImageDraw
import torch.multiprocessing as mp
cv2.setNumThreads(0)
def cv2_video_info(video_path):
vid = cv2.VideoCapture(video_path)
width = vid.get(cv2.CAP_PROP_FRAME_WIDTH)
height = vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
fps = vid.get(cv2.CAP_PROP_FPS)
frame_num = vid.get(cv2.CAP_PROP_FRAME_COUNT)
vid.release()
return dict(
width=int(width),
height=int(height),
fps=fps,
frame_num=int(frame_num),
)
class AVAVisualizer(object):
# category names are modified for better visualization
CATEGORIES = [
"bend/bow",
"crawl", #
"crouch/kneel",
"dance",
"fall down",
"get up",
"jump/leap",
"lie/sleep",
"martial art",
"run/jog",
"sit",
"stand",
"swim",
"walk",
"answer phone",
"brush teeth", #
"carry/hold sth.",
"catch sth.", #
"chop", #
"climb",
"clink glass", #
"close",
"cook", #
"cut",
"dig", #
"dress/put on clothing",
"drink",
"drive",
"eat",
"enter",
"exit", #
"extract", #
"fishing", #
"hit sth.",
"kick sth.", #
"lift/pick up",
"listen to sth.",
"open",
"paint", #
"play board game", #
"play musical instrument",
"play with pets", #
"point to sth.",
"press", #
"pull sth.",
"push sth.",
"put down",
"read",
"ride",
"row boat", #
"sail boat",
"shoot",
"shovel", #
"smoke",
"stir", #
"take a photo",
"look at a cellphone",
"throw",
"touch sth.",
"turn",
"watch screen",
"work on a computer",
"write",
"fight/hit sb.",
"give/serve sth. to sb.",
"grab sb.",
"hand clap",
"hand shake",
"hand wave",
"hug sb.",
"kick sb.", #
"kiss sb.",
"lift sb.",
"listen to sb.",
"play with kids", #
"push sb.",
"sing",
"take sth. from sb.",
"talk",
"watch sb.",
]
COMMON_CATES = [
'dance',
'run/jog',
'sit',
'stand',
'swim',
'walk',
'answer phone',
'carry/hold sth.',
'drive',
'play musical instrument',
'ride',
'fight/hit sb.',
'listen to sb.',
'talk',
'watch sb.'
]
EXCLUSION = [
"crawl",
"brush teeth",
"catch sth.",
"chop",
"clink glass",
"cook",
"dig",
"exit",
"extract",
"fishing",
"kick sth.",
"paint",
"play board game",
"play with pets",
"press",
"row boat",
"shovel",
"stir",
"kick sb.",
"play with kids",
]
def __init__(
self,
video_path,
output_path,
realtime,
start,
duration,
show_time,
confidence_threshold=0.5,
exclude_class=None,
common_cate=False,
):
self.vid_info = cv2_video_info(video_path)
fps = self.vid_info["fps"]
if fps == 0 or fps > 100:
print(
"Warning: The detected frame rate {} could be wrong. The behavior of this demo code can be abnormal.".format(
fps))
self.realtime = realtime
self.start = start
self.duration = duration
self.show_time = show_time
self.confidence_threshold = confidence_threshold
if common_cate:
self.cate_to_show = self.COMMON_CATES
self.category_split = (6, 11)
else:
self.cate_to_show = self.CATEGORIES
self.category_split = (14, 63)
self.cls2label = {class_name: i for i, class_name in enumerate(self.cate_to_show)}
if exclude_class is None:
exclude_class = self.EXCLUSION
self.exclude_id = [self.cls2label[cls_name] for cls_name in exclude_class if cls_name in self.cls2label]
self.width = self.vid_info["width"]
self.height = self.vid_info["height"]
long_side = min(self.width, self.height)
self.font_size = max(int(round((long_side / 40))), 1)
self.box_width = max(int(round(long_side / 180)), 1)
self.font = ImageFont.truetype("./Roboto-Bold.ttf", self.font_size)
self.box_color = (191, 40, 41)
self.category_colors = ((176, 85, 234), (87, 118, 198), (52, 189, 199))
self.category_trans = int(0.6 * 255)
self.action_dictionary = dict()
if realtime:
# Output Video
width = self.vid_info["width"]
height = self.vid_info["height"]
fps = self.vid_info["fps"]
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
self.out_vid = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
else:
self.frame_queue = mp.JoinableQueue(512)
self.result_queue = mp.JoinableQueue()
self.track_queue = mp.JoinableQueue()
self.done_queue = mp.Queue()
self.frame_loader = mp.Process(target=self._load_frame, args=(video_path,))
self.frame_loader.start()
self.video_writer = mp.Process(target=self._wirte_frame, args=(output_path,))
self.video_writer.start()
def realtime_write_frame(self, result, orig_img, boxes, scores, ids):
orig_img = orig_img[:, :, ::-1]
if result is not None:
result, timestamp, result_ids = result
update_boxes = result.bbox
update_scores = result.get_field("scores")
update_ids = result_ids
if update_boxes is not None:
self.update_action_dictionary(update_scores, update_ids)
if boxes is not None:
last_visual_mask = self.visual_result(boxes, ids)
orig_img = self.visual_frame(orig_img, last_visual_mask)
cv2.imshow("my webcam", orig_img)
self.out_vid.write(orig_img)
if cv2.waitKey(1) == 27:
return False
return True
def _load_frame(self, video_path):
vid = cv2.VideoCapture(video_path)
vid.set(cv2.CAP_PROP_POS_MSEC, self.start)
vid_avail = True
while True:
vid_avail, frame = vid.read()
if not vid_avail:
break
mills = vid.get(cv2.CAP_PROP_POS_MSEC)
if self.duration != -1 and mills > self.start + self.duration:
break
self.frame_queue.put((frame, mills))
vid.release()
self.frame_queue.put("DONE")
self.frame_queue.join()
self.frame_queue.close()
# tqdm.write("load frame closed")
def _wirte_frame(self, output_path):
width = self.vid_info["width"]
height = self.vid_info["height"]
fps = self.vid_info["fps"]
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out_vid = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
has_frame = True
result = self.result_queue.get()
timestamp = float('inf')
result_ids = None
if not isinstance(result, str):
result, timestamp, result_ids = result
while has_frame:
track_result = self.track_queue.get()
# read frame
data = self.frame_queue.get()
self.frame_queue.task_done()
if isinstance(result, str) and data == "DONE":
self.track_queue.task_done()
self.result_queue.task_done()
break
# note that the timestamp should be in milliseconds
frame, mills = data
if self.show_time:
frame = self.visual_timestampe(frame, mills)
if mills - timestamp + 0.5 > 0:
# print("renew action_dict:{}".format(self.action_dictionary))
boxes = result.bbox
scores = result.get_field("scores")
ids = result_ids
self.result_queue.task_done()
result = self.result_queue.get()
if not isinstance(result, str):
result, timestamp, result_ids = result
else:
timestamp = float('inf')
else:
boxes, ids = track_result
scores = None
if boxes is not None:
self.update_action_dictionary(scores, ids)
last_visual_mask = self.visual_result(boxes, ids)
new_frame = self.visual_frame(frame, last_visual_mask)
out_vid.write(new_frame)
else:
out_vid.write(frame)
self.track_queue.task_done()
self.done_queue.put(True)
out_vid.release()
tqdm.write("The output video has been written to the disk.")
def hou_min_sec(self, total_millis):
total_millis = int(total_millis)
millis = total_millis % 1000
total_millis /= 1000
seconds = total_millis % 60
total_millis /= 60
minutes = total_millis % 60
total_millis /= 60
hours = total_millis
return ("%02d:%02d:%02d.%03d" % (hours, minutes, seconds, millis))
def visual_timestampe(self, frame, mills):
time_text = self.hou_min_sec(mills)
img = Image.fromarray(frame[..., ::-1])
img = img.convert("RGBA")
overlay = Image.new("RGBA", img.size, (0, 0, 0, 0))
trans_draw = ImageDraw.Draw(overlay)
text_width, text_height = trans_draw.textsize(time_text, font=self.font)
width_pad = max(self.font_size // 2, 1)
rec_height = int(round(1.8 * text_height))
height_pad = round((rec_height - text_height) / 2)
r_x1 = 0
r_y2 = img.height
r_x2 = r_x1 + text_width + width_pad * 2
r_y1 = r_y2 - rec_height
rec_pos = (r_x1, r_y1, r_x2, r_y2)
text_pos = (r_x1 + width_pad, r_y1 + height_pad)
trans_draw.rectangle(rec_pos, fill=(0, 0, 0, self.category_trans))
trans_draw.text(text_pos, time_text, fill=(255, 255, 255, self.category_trans), font=self.font, align="center")
img = Image.alpha_composite(img, overlay)
img = img.convert("RGB")
return np.array(img)[..., ::-1]
def update_action_dictionary(self, scores, ids):
# Update action_dictionary
if scores is not None:
for score, id in zip(scores, ids):
show_idx = torch.nonzero(score >= self.confidence_threshold, as_tuple=False).squeeze(1)
captions = []
bg_colors = []
#captions.append("id: {}".format(int(id)))
#bg_colors.append(0)
for category_id in show_idx:
if category_id in self.exclude_id:
continue
label = self.cate_to_show[category_id]
conf = " %.2f" % score[category_id]
caption = label + conf
captions.append(caption)
if category_id < self.category_split[0]:
bg_colors.append(0)
elif category_id < self.category_split[1]:
bg_colors.append(1)
else:
bg_colors.append(2)
self.action_dictionary[int(id)] = {
"captions": captions,
"bg_colors": bg_colors,
}
def visual_result(self, boxes, ids):
bboxes = boxes
ids = ids
result_vis = Image.new("RGBA", (self.width, self.height), (0, 0, 0, 0))
draw = ImageDraw.Draw(result_vis)
for box in bboxes:
draw.rectangle(box.tolist(), outline=self.box_color + (255,), width=self.box_width)
for box, id in zip(bboxes, ids):
caption_and_color = self.action_dictionary.get(int(id), None)
if caption_and_color is None:
captions = []
bg_colors = []
else:
captions = caption_and_color['captions']
bg_colors = caption_and_color['bg_colors']
if len(captions) == 0:
continue
x1, y1, x2, y2 = box.tolist()
overlay = Image.new("RGBA", result_vis.size, (0, 0, 0, 0))
trans_draw = ImageDraw.Draw(overlay)
caption_sizes = [trans_draw.textsize(caption, font=self.font) for caption in captions]
caption_widths, caption_heights = list(zip(*caption_sizes))
max_height = max(caption_heights)
rec_height = int(round(1.8 * max_height))
space_height = int(round(0.2 * max_height))
total_height = (rec_height + space_height) * (len(captions) - 1) + rec_height
width_pad = max(self.font_size // 2, 1)
start_y = max(round(y1) - total_height, space_height)
for i, caption in enumerate(captions):
r_x1 = round(x1)
r_y1 = start_y + (rec_height + space_height) * i
r_x2 = r_x1 + caption_widths[i] + width_pad * 2
r_y2 = r_y1 + rec_height
rec_pos = (r_x1, r_y1, r_x2, r_y2)
height_pad = round((rec_height - caption_heights[i]) / 2)
text_pos = (r_x1 + width_pad, r_y1 + height_pad)
trans_draw.rectangle(rec_pos, fill=self.category_colors[bg_colors[i]] + (self.category_trans,))
trans_draw.text(text_pos, caption, fill=(255, 255, 255, self.category_trans), font=self.font,
align="center")
result_vis = Image.alpha_composite(result_vis, overlay)
return result_vis
def visual_frame(self, frame, visual_mask):
img = Image.fromarray(frame[..., ::-1])
img = img.convert("RGBA")
img = Image.alpha_composite(img, visual_mask)
img = img.convert("RGB")
return np.array(img)[..., ::-1]
def visual_frame_old(self, frame, result):
bboxes = result.bbox
scores = result.get_field("scores")
img = Image.fromarray(frame[..., ::-1])
img = img.convert("RGBA")
draw = ImageDraw.Draw(img)
for box in bboxes:
draw.rectangle(box.tolist(), outline=self.box_color + (255,), width=self.box_width)
for box, score in zip(bboxes, scores):
show_idx = torch.nonzero(score >= self.confidence_threshold, as_tuple=False).squeeze(1)
captions = []
bg_colors = []
for category_id in show_idx:
if category_id in self.exclude_id:
continue
label = self.cate_to_show[category_id]
conf = " %.2f" % score[category_id]
caption = label + conf
captions.append(caption)
if category_id <= self.category_split[0]:
bg_colors.append(0)
elif category_id <= self.category_split[1]:
bg_colors.append(1)
else:
bg_colors.append(2)
x1, y1, x2, y2 = box.tolist()
overlay = Image.new("RGBA", img.size, (0, 0, 0, 0))
trans_draw = ImageDraw.Draw(overlay)
caption_sizes = [trans_draw.textsize(caption, font=self.font) for caption in captions]
caption_widths, caption_heights = list(zip(*caption_sizes))
max_height = max(caption_heights)
rec_height = int(round(1.8 * max_height))
space_height = int(round(0.2 * max_height))
total_height = (rec_height + space_height) * (len(captions) - 1) + rec_height
width_pad = max(self.font_size // 2, 1)
start_y = max(round(y1) - total_height, space_height)
for i, caption in enumerate(captions):
r_x1 = round(x1)
r_y1 = start_y + (rec_height + space_height) * i
r_x2 = r_x1 + caption_widths[i] + width_pad * 2
r_y2 = r_y1 + rec_height
rec_pos = (r_x1, r_y1, r_x2, r_y2)
height_pad = round((rec_height - caption_heights[i]) / 2)
text_pos = (r_x1 + width_pad, r_y1 + height_pad)
trans_draw.rectangle(rec_pos, fill=self.category_colors[bg_colors[i]] + (self.category_trans,))
trans_draw.text(text_pos, caption, fill=(255, 255, 255, self.category_trans), font=self.font,
align="center")
img = Image.alpha_composite(img, overlay)
img = img.convert("RGB")
return np.array(img)[..., ::-1]
def send(self, result):
self.result_queue.put(result)
def send_track(self, result):
self.track_queue.put(result)
def close(self):
if self.realtime:
self.out_vid.release()
else:
self.result_queue.join()
self.result_queue.close()
self.track_queue.join()
self.track_queue.close()
def progress_bar(self, total):
# get initial
cnt = 0
while not self.done_queue.empty():
_ = self.done_queue.get()
cnt += 1
pbar = tqdm(total=total, initial=cnt, desc="Video Writer", unit=" frame")
# update bar
while cnt < total:
_ = self.done_queue.get()
cnt += 1
pbar.update(1)
# close bar
pbar.close()