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()
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:
break
capture.release()
output.release()
cv2.destroyAllWindows()
print(datetime.now() - startTime1)
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
if cv2.waitKey(1) & 0xFF == ord('q'):
break
elif ret and masking > 0:
masking = masking + 1
if masking == 2: # 몇 프레임 당 Compute할것인지
masking = 0
if cv2.waitKey(1) & 0xFF == ord(
'q'): # waitkey & 0xFF = 1111 1111 == 'q'
break
output.write(frame) # Model forward Compute를 거치지 않고 바로 출력
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()
else:
if abs(h-h_prev2) < threshold and abs(w-w_prev2) < threshold:
h_prev, w_prev, i_prev = h_prev2, w_prev2, i_prev2
else:
remove.append(i)
for r in list(reversed(remove)):
print ("Deleted row ", r)
r['class_ids'] = np.delete(r['class_ids'], (r), axis=0)
r['rois'] = np.delete(r['rois'], (r), axis=0)
r['masks'] = np.delete(r['masks'], (r), axis=0)
r['scores'] = np.delete(r['scores'], (r), axis=0)
if h_prev2 is not None:
print ("Deleted row ", i_prev2)
r['class_ids'] = np.delete(r['class_ids'], (i_prev2), axis=0)
r['rois'] = np.delete(r['rois'], (i_prev2), axis=0)
r['masks'] = np.delete(r['masks'], (i_prev2), axis=0)
r['scores'] = np.delete(r['scores'], (i_prev2), axis=0)
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
class_names, r['scores'], save=args.save_demo, writer=out, dtype=t)
if args.image is not None and not args.save_demo or args.image_dir is not None:
c = cv2.waitKey()
if args.image_dir is not None:
if c==81:
ind-=2
if args.video is not None:
cap.release()
out.release()
cv2.destroyAllWindows()
actual_powerbanks = 0 if len(actual_powerbanks)==0 else actual_powerbanks[0]
unique, counts = np.unique(cls2, return_counts=True)
rotated_powerbanks = counts[unique==powerbank_index]
rotated_powerbanks = 0 if len(rotated_powerbanks)==0 else rotated_powerbanks[0]
if actual_powerbanks < rotated_powerbanks:
for _ in range(rotated_powerbanks - actual_powerbanks):
cls1 = np.append(cls1,powerbank_index)
scores1 = np.append(scores1, '0.99') #placeholder values
bboxes1.append([0,0,5,5]) #placeholder values to be changed for visualization if necessary
image = imutils.rotate_bound(image, 90)
'''
visualize.display_instances(image,
np.array(bboxes),
None,
classes,
class_names,
scores,
save=args.save_demo,
writer=out,
ind=jnd)
if args.image is not None and not args.save_demo or args.image_dir is not None:
cv2.waitKey()
if args.video is not None:
cap.release()
out.release()
cv2.destroyAllWindows()
