khazit / lip2word Goto Github PK
View Code? Open in Web Editor NEWEnd-to-end pipeline for lip reading at the word level using a tensorflow CNN implementation.
End-to-end pipeline for lip reading at the word level using a tensorflow CNN implementation.
Hello. I want to see the results of the pretrained model. Can you send it by email?
My E-mail is [email protected]
Thank you.
hey!
Can you send me the pre-train model....My laptop lacks the hardware requirements.
My email id - [email protected]
Could you please send me link for downloading model.. I'm lack of dataset as well as hardware requirements (though I can use Google Colab but i don't know how to load such large dataset into Colab for training)
Hey! I really am interested in your code and was wondering if you could upload your pretrained model so that I can run some tests on it. My computer isn't really strong enough to train it from scratch.
Hoping for a quick reply,
In frameAdjust function defination, please add else block and push the code as mentioned below:
if target == n_frames :
print("Perfect number of frames !")
return video
else :
if n_frames > target :
# If number of frames is more than 29, we select
# 29 evenly distributed frames
print("Adjusting number of frames")
idx = np.linspace(0, n_frames-1, 29)
idx = np.around(idx, 0).astype(np.int32)
print("Indexes of the selected frames : \n{}".format(idx))
return video[:, :, idx]
else :
# If number of frames is less than 29, duplicate last
# frame at the end of the video
output_video = np.zeros((video.shape[0], video.shape[1], 29)).astype(np.uint8)
output_video[:, :, :n_frames] = video
for i in range(target-n_frames+1) :
output_video[:, :, i+n_frames-1] = output_video[:, :, n_frames-1]
return output_video
Could you please send me link for downloading model.. I'm lack of dataset as well as hardware requirements.
In capture_process_frames function, it will not be able to manage more than 29 frames, hence have a condition check or push it under try block! Check the Code below
while vidObj.isOpened():
success, image = vidObj.read()
if success :
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = resize_frame(image, size_frame-180, offset=35)
image = cv2.resize(image, dsize=(size, size), interpolation=cv2.INTER_LINEAR)
try:
all_frames[countsize:size(count+1), :] = image
except:
break
count += 1
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