def run_inference_on_image():
answer = None
if not tf.gfile.Exists(imagePath):
tf.logging.fatal('File does not exist %s', imagePath)
return answer
image_data = tf.gfile.FastGFile(imagePath, 'rb').read()
# 저장된(saved) GraphDef 파일로부터 graph를 생성한다.
create_graph()
with tf.Session() as sess:
f = open(labelsFullPath, 'rb')
lines = f.readlines()
labels = [str(w).replace("\n", "") for w in lines]
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
cap = cv2.VideoCapture(0)
while True:
start = time.time()
ret, frame = cap.read()
frame = hs.handsegment(frame)
cv2.imwrite('./test.jpg', frame)
image_data = tf.gfile.FastGFile(imagePath, 'rb').read()
predictions = sess.run(softmax_tensor,
{'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort(
)[-5:][::-1] # 가장 높은 확률을 가진 5개(top 5)의 예측값(predictions)을 얻는다.
for node_id in top_k:
human_string = labels[node_id]
score = predictions[node_id]
print('%s (score = %.5f)' % (human_string, score))
answer = labels[top_k[0]]
print("ans : " + str(answer))
print("time : ", time.time() - start)
if len(frames) < FRAMES_PER_VIDEO:
print('[WARNING] Not enough frames. At least %d frames' %
(FRAMES_PER_VIDEO))
state = 0
else:
frames = np.array(frames)
ind = np.arange(0, len(frames),
len(frames) / FRAMES_PER_VIDEO).astype(int)
frames = frames[ind]
print('index', ind)
print('No of frames', frames.shape)
for i in range(len(frames)):
f = frames[i]
f = cv2.resize(f, SIZE)
f = handsegment.handsegment(f)
reprocessed_frames.append(f)
cv2.imwrite(folder + '/unknown_frame_' + str(i) + '.jpeg', f)
state = 4
start_time = time.time()
elif state == 4:
# for f in reprocessed_frames:
# cv2.imshow('after reprocessing', f)
# cv2.waitKey(1)
# PREDICT_SPATIAL
from predict_spatial import *
print('[] PREDICT SPATIAL ')
def convert(dataset):
fcount = 0
rootPath = os.getcwd()
majorData = os.path.join(os.getcwd(), "majorData")
if (not exists(majorData)):
os.makedirs(majorData)
dataset = os.path.join(os.getcwd(), dataset)
os.chdir(dataset)
x = os.listdir(os.getcwd())
for i in range(len(x)):
if x[i] == "test":
x = x[i]
x.remove(".DS_Store")
print(x)
for gesture in x:
adhyan = gesture
gesture = os.path.join(dataset, gesture)
os.chdir(gesture)
frames = os.path.join(majorData, adhyan)
if (not os.path.exists(frames)):
os.makedirs(frames)
videos = os.listdir(os.getcwd())
videos = [video for video in videos if (os.path.isfile(video))]
for video in videos:
name = os.path.abspath(video)
fcount = fcount + 1
print(fcount, " : ", name)
cap = cv2.VideoCapture(name) # capturing input video
frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
print(frameCount)
count = 0
os.chdir(frames)
lastFrame = None
while (1):
ret, frame = cap.read() # extract frame
if ret is False:
break
framename = os.path.splitext(video)[0]
framename = framename + "_frame_" + str(count) + ".jpeg"
hc.append([join(frames, framename), adhyan, frameCount])
if (not os.path.exists(framename)):
frame = hs.handsegment(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
lastFrame = frame
cv2.imwrite(framename, frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
count += 1
while (count < 201):
framename = os.path.splitext(video)[0]
framename = framename + "_frame_" + str(count) + ".jpeg"
hc.append([join(frames, framename), adhyan, frameCount])
if (not os.path.exists(framename)):
cv2.imwrite(framename, lastFrame)
count += 1
os.chdir(gesture)
cap.release()
cv2.destroyAllWindows()
print(hc)
os.chdir(rootPath)
with open('data/labeled-frames-1.pkl', 'wb') as handle:
# with open('data/labeled-frames-2.pkl', 'wb') as handle:
pickle.dump(hc, handle, protocol=pickle.HIGHEST_PROTOCOL)
def convert(gesture_folder, target_folder):
rootPath = os.getcwd()
majorData = os.path.abspath(target_folder)
if not exists(majorData):
os.makedirs(majorData)
gesture_folder = os.path.abspath(gesture_folder)
os.chdir(gesture_folder)
gestures = os.listdir(os.getcwd())
print("Source Directory containing gestures: %s" % (gesture_folder))
print("Destination Directory containing frames: %s\n" % (majorData))
for gesture in tqdm(gestures, unit='actions', ascii=True):
gesture_path = os.path.join(gesture_folder, gesture)
os.chdir(gesture_path)
gesture_frames_path = os.path.join(majorData, gesture)
if not os.path.exists(gesture_frames_path):
os.makedirs(gesture_frames_path)
videos = os.listdir(os.getcwd())
videos = [video for video in videos if(os.path.isfile(video))]
for video in tqdm(videos, unit='videos', ascii=True):
name = os.path.abspath(video)
cap = cv2.VideoCapture(name) # capturing input video
frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
lastFrame = None
os.chdir(gesture_frames_path)
count = 1
# assumption only first 150 frames are important
while count < 151:
ret, frame = cap.read() # extract frame
if ret is False:
break
framename = os.path.splitext(video)[0]
framename = framename + "_frame_" + str(count) + ".jpeg"
hc.append([join(gesture_frames_path, framename), gesture, frameCount])
if not os.path.exists(framename):
frame = hs.handsegment(frame)
lastFrame = frame
cv2.imwrite(framename, frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
count += 1
# repeat last frame untill we get 200 frames
while count < 151:
framename = os.path.splitext(video)[0]
framename = framename + "_frame_" + str(count) + ".jpeg"
hc.append([join(gesture_frames_path, framename), gesture, frameCount])
if not os.path.exists(framename):
cv2.imwrite(framename, lastFrame)
count += 1
os.chdir(gesture_path)
cap.release()
cv2.destroyAllWindows()
os.chdir(rootPath)