class VideoProcessorThread:
def __init__(self, path, queueSize=3000):
self.path = path
# self.video = UMatFileVideoStream(self.path, 128).start()
# self.rgb = cv2.UMat(self.height, self.width, cv2.CV_8UC3)
self.cap = cv2.VideoCapture(path)
self.stopped = False
# # initialize the queue used to store frames read from
# # the video file
self.Q = Queue(maxsize=queueSize)
self.fgbg = cv2.createBackgroundSubtractorMOG2(history=500,
varThreshold=200,
detectShadows=False)
self.height, self.width = int(self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.resize_size = (int(self.width * resize_ratio),
int(self.height * resize_ratio))
self.IP = HumanDetection(self.resize_size)
def stop(self):
# indicate that the thread should be stopped
self.stopped = True
def more(self):
# return True if there are still frames in the queue
return self.Q.qsize() > 0
def read(self):
# return next frame in the queue
return self.Q.get()
def update(self):
# keep looping infinitely
self.IP.init()
# IP.object_tracker.init_tracker()
cnt = 0
while True:
# if the thread indicator variable is set, stop the
# thread
if self.stopped:
return
# otherwise, ensure the queue has room in it
if not self.Q.full():
# read the next frame from the file
(grabbed, frame) = self.cap.read()
start = time.time()
if grabbed:
frame = cv2.resize(frame, self.resize_size)
frame2 = copy.deepcopy(frame)
kps, boxes, kps_score = self.IP.process_img(frame,
gray=gray)
img, img_black = self.IP.visualize()
if classify_type == 1:
result = self.IP.classify_whole(img_black, img)
elif classify_type == 2:
result = self.IP.classify_whole(frame2, img)
elif classify_type == 3:
result = self.IP.classify(img_black, img, boxes)
elif classify_type == 4:
result = self.IP.classify(frame2, img, boxes)
else:
raise ValueError("Not a right classification type!")
cv2.imshow("res", cv2.resize(img, show_size))
cv2.waitKey(2)
all_time = time.time() - start
print("time is:", all_time)
# if the `grabbed` boolean is `False`, then we have
# reached the end of the video file
if not grabbed:
self.stop()
return
# add the frame to the queue
self.Q.put(frame)
else:
self.Q.queue.clear()
class VideoProcessor:
def __init__(self, video_path):
self.cap = cv2.VideoCapture(video_path)
self.height, self.width = int(self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.resize_size = (int(self.width * resize_ratio),
int(self.height * resize_ratio))
self.IP = ImgProcessor(self.resize_size)
if config.write_video:
try:
self.out = cv2.VideoWriter(video_path[:-4] + "_processed.avi",
fourcc, 15,
(self.width, self.height))
self.res_out = cv2.VideoWriter(
video_path[:-4] + "_processed_res.avi", fourcc, 15,
(self.width, self.height))
except:
self.out = cv2.VideoWriter("output.avi", fourcc, 15,
(self.width, self.height))
self.res_out = cv2.VideoWriter("output_res.avi", fourcc, 15,
(self.width, self.height))
if config.write_box:
box_file = "/".join(video_path.split(
"/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_box.txt"
self.box_txt = open(box_file, "w")
if config.write_kps:
kps_file = "/".join(video_path.split(
"/")[:-1]) + "/" + video_path.split("/")[-1][:-4] + "_kps.txt"
kps_score_file = "/".join(
video_path.split("/")[:-1]) + "/" + video_path.split(
"/")[-1][:-4] + "_kps_score.txt"
self.kps_txt = open(kps_file, "w")
self.kps_score_txt = open(kps_score_file, "w")
def process_video(self):
self.IP.init()
cnt = 0
while True:
ret, frame = self.cap.read()
cnt += 1
if ret:
frame = cv2.resize(frame, self.resize_size)
frame2 = copy.deepcopy(frame)
kps, boxes, kps_score = self.IP.process_img(frame, gray=gray)
img, img_black = self.IP.visualize()
if classify_type == 1:
result = self.IP.classify_whole(img_black, img)
elif classify_type == 2:
result = self.IP.classify_whole(frame2, img)
elif classify_type == 3:
result = self.IP.classify(img_black, img, boxes)
elif classify_type == 4:
result = self.IP.classify(frame2, img, boxes)
else:
raise ValueError("Not a right classification type!")
if boxes is not None:
if config.write_box:
box_str = ""
for k, v in boxes.items():
box_str += boxdict2str(k, v)
self.box_txt.write(box_str)
self.box_txt.write("\n")
else:
if config.write_box:
self.box_txt.write("\n")
if kps:
# cv2.putText(img, "cnt{}".format(cnt), (100, 200), cv2.FONT_HERSHEY_PLAIN, 5, (0, 255, 255), 5)
if config.write_kps:
kps_str = ""
for k, v in kps.items():
kps_str += kpsdict2str(k, v)
self.kps_txt.write(kps_str)
self.kps_txt.write("\n")
kps_score_str = ""
for k, v in kps_score.items():
kps_score_str += kpsScoredict2str(k, v)
self.kps_score_txt.write(kps_score_str)
self.kps_score_txt.write("\n")
else:
if config.write_kps:
self.kps_txt.write("\n")
self.kps_score_txt.write("\n")
img = frame
# cv2.putText(img, "cnt{}".format(cnt), (100, 200), cv2.FONT_HERSHEY_PLAIN, 5, (0, 255, 255), 5)
cv2.imshow("res", cv2.resize(img, show_size))
cv2.waitKey(1)
if config.write_video:
self.out.write(cv2.resize(frame, store_size))
else:
self.cap.release()
if config.write_video:
self.out.release()
break