def button2Function(self):
capture = cv2.VideoCapture(0)
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
self.lb_3.setText("*****진행중*****")
self.lb_3.repaint()
while True:
ret, frame = capture.read()
results = model.detect([frame], verbose=0)
r = results[0]
# frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])
cv2.imshow('frame', frame)
k = cv2.waitKey(0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
elif k == ord('s'):
cv2.VideoWriter('mycam.avi', codec, fc,
(int(cap.get(3)), int(cap.get(4))))
capture.release()
cv2.destroyAllWindows()
self.lb_4.setText("완료")
self.lb_4.repaint()
capture.release()
cv2.destroyAllWindows()
self.lb_4.setText("완료")
self.lb_4.repaint()
def button1Function(self):
capture = cv2.VideoCapture("kmm.mov")
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
codec = cv2.VideoWriter_fourcc(*'DIVX')
output = cv2.VideoWriter('1.mp4', codec, 60.0, size)
print("btn_1 Clicked!!!!!!!!!!222")
self.lb_3.setText("*****진행중*****")
self.lb_3.repaint()
print("btn_1 Clicked!!!!!!!!!!333")
while (capture.isOpened()):
print("btn_1 Clicked!!!!!!!!!!444444")
ret, frame = capture.read()
if ret:
print("btn_1 Clicked!!!!!!!!55555555")
# add mask to frame
results = model.detect([frame], verbose=1)
r = results[0]
masks = r['masks'][:, :, r['class_ids'] == 44]
mask = np.sum(masks, axis=2).astype(np.bool)
mask_3d = np.repeat(np.expand_dims(mask, axis=2), 3,
axis=2).astype(np.uint8)
blurred_img = cv2.blur(frame, (101, 101)) #블러
mask_3d_blurred = cv2.medianBlur(mask_3d, 9)
person_mask = mask_3d_blurred * blurred_img.astype(
np.float32) #배경 그대로, 사람 블러
bg_mask = (1 - mask_3d_blurred) * frame.astype(np.float32)
out = (person_mask + bg_mask).astype(np.uint8)
output.write(out)
out = cv2.resize(
out,
(int(out.shape[1] * scaler), int(out.shape[0] * scaler)))
cv2.imshow('out', out)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("btn_1 Clicked!!!!!!!!!!666666")
break
else:
break
capture.release()
output.release()
cv2.destroyAllWindows()
self.lb_4.setText("완료")
self.lb_4.repaint()
capture = cv2.VideoCapture(
'D:\SemanticSegmentation\Mask_RCNN\chicken_beautiful.mp4')
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
codec = cv2.VideoWriter_fourcc(*'DIVX')
output = cv2.VideoWriter(r'C:\Users\surface\Desktop\videofile_masked.avi',
codec, 25.0, size)
while (capture.isOpened()):
ret, frame = capture.read()
if ret:
# add mask to frame
print("yes")
results = model.detect([frame], verbose=0)
r = results[0]
frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'])
output.write(frame)
print("finish")
# cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
capture.release()
output.release()
cv2.destroyAllWindows()
else:
break
capture.release()
output.release()
cv2.destroyAllWindows()
print(datetime.now() - startTime1)"""
images = []
while (capture.isOpened()):
ret, frame = capture.read()
if ret:
# add mask to frame
images.append(frame)
if len(images) == 32:
startTime = datetime.now()
results = model.detect(images, verbose=1)
print("il a fallu ", (datetime.now() - startTime),
" secondes soit ", (datetime.now() - startTime) / 32,
" par image")
for i, item in enumerate(zip(images, results)):
image = item[0]
r = item[1]
frame = display_instances(image, r['rois'], r['masks'],
r['class_ids'], class_names, None)
output.write(frame)
#cv2.imshow('frame', frame)
images = []
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
output = cv2.VideoWriter('videofile_masked_road_20%35%_2x_50_inc.avi', codec,
30.0, size)
flag = 0
masking = 0 # 인코딩(Boxing) 처리 성능 향상을 위해서 한프레임씩 건너서 Boxing(Object Detecting) -> 속도 향상
print("Start masking")
now = datetime.now()
print("Start at :", now)
start = round(time.time())
#
while (1): #capture.isOpened()
ret, frame = capture.read() # ret 받은 이미지가 있는지 여부 , 각 프레임 받기
if ret and masking == 0:
results = model.detect(
[frame],
verbose=0) # 모델 사용 -> 모델에서 Forward Compute 해서 Detection 결과를 반환
r = results[0]
# print("visualize_cv2 LINE 131 :", r)
# print("class names :", class_names)
#{'rois': array([[1061, 11, 1280, 201],
masking = masking + 1
frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'], Video_w,
Video_w_20, Video_w_80, Video_h_35)
# display_instances를 호출(수행)할 때 마다
output.write(frame)
cv2.imshow('frame', frame) #원본 영상에 Masking이 입혀진 영상 보여주기 함수
def process_clip(video):
## Calculate video duration
v = cv2.VideoCapture(video)
v.set(cv2.CAP_PROP_POS_AVI_RATIO, 1)
duration = v.get(cv2.CAP_PROP_POS_MSEC)
frameCount = int(v.get(cv2.CAP_PROP_FRAME_COUNT))
print('duration = {}'.format(duration))
print('number of frames = {}'.format(frameCount))
# the 1st frame is frame 0, not 1, so "5335" means after the last frame
POS_FRAMES = v.get(cv2.CAP_PROP_POS_FRAMES)
FRAME_COUNT = v.get(cv2.CAP_PROP_FRAME_COUNT)
print('POS_FRAMES = ' + str(POS_FRAMES))
print('FRAME_COUNT = ' + str(FRAME_COUNT))
v.release()
## Output video frame time, frame content, and bounding boxes to dictionary
capture = cv2.VideoCapture(video)
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
codec = cv2.VideoWriter_fourcc(*'DIVX')
fps = 30
output = cv2.VideoWriter('{}_masked.avi'.format(video), codec, fps,
(640, 360))
count = 1
out_dict = {}
while (capture.isOpened()):
# cap.read() returns a bool (True/False).
# If frame is read correctly, it will be True. So you can check end of the video by checking this return value
ret, frame = capture.read()
if ret:
road_frame = frame[0:360, 640:1280]
# add mask to frame
results = model.detect([road_frame], verbose=0)
r = results[0]
road_frame = display_instances(road_frame, r['rois'], r['masks'],
r['class_ids'], class_names,
r['scores'])
output.write(road_frame)
out_dict[count] = {}
out_dict[count]['cls_id'] = r['class_ids']
out_dict[count]['rois'] = r['rois']
print('[INFO] Frame {}/{}'.format(count, frameCount))
count += 1
else:
break
capture.release()
output.release()
## Put the video data into a JSON file
frame2content = {}
for k, v in out_dict.items():
objs = [class_names[i] for i in v["cls_id"]]
obj2num = dict(collections.Counter(objs))
_objs = []
for o in objs:
_objs.append(o + "_" + str(obj2num[o]))
obj2num[o] -= 1
obj2roi = {}
for idx, o in enumerate(_objs):
obj2roi[o] = list(map(int, list(v["rois"][idx, :])))
frame2content[k] = obj2roi
json_log = open('{}.json'.format(video), 'w')
json.dump(frame2content, json_log)
json_log.close()
import numpy as np
from visualize_cv2 import model, display_instances, class_names
capture = cv2.VideoCapture('videofile.mp4') #read video
size = (
int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), #get width, height
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
codec = cv2.VideoWriter_fourcc(*'DIVX')
output = cv2.VideoWriter('videofile_masked.avi', codec, 60.0,
size) #write video,fps, frame size
while (capture.isOpened()):
ret, frame = capture.read()
if ret:
# add mask to frame
results = model.detect([frame],
verbose=0) #frame send to model and get result
r = results[0]
frame = display_instances(frame, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'])
output.write(frame) #output writing to file
cv2.imshow('frame', frame) #display frame
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
capture.release()
output.release()
cv2.destroyAllWindows()
