How do you use tflite model for camera video frames? about mai-vsr-diggers HOT 12 CLOSED

Hello, First, I would like to appreciate your work. Actually, I have been trying to implement this model for my custom project. I have tried to obtain the single frame result from model_none.tflite, but it takes 10 frames, which I did. However, the result does not look good. can you tell me why? [image: download] <https://user-images.githubusercontent.com/94444066/144837147-f82758f8-2d16-4f71-b6cd-6383dfe71884.png> I have used the following: 1. For stacking all frames in the color channel which gives (1, 180, 320, 300) all_frames = sorted(glob.glob('/content/drive/MyDrive/Mobile_communication/val_sharp_bicubic/X4/000/*.png')) read_1 = tf.io.read_file(all_frames[0]) read_1 = tf.image.decode_jpeg(read_1, channels=3) stacked = tf.Variable(np.empty((1,read_1.shape[0],read_1.shape[1],read_1.shape[2]), dtype=np.float32)) #for ind in range(1): for ind in all_frames: test_img_path = ind lr1 = tf.io.read_file(test_img_path) lr = tf.image.decode_jpeg(lr1, channels=3) lr = tf.expand_dims(lr, axis=0) lr = tf.cast(lr, tf.float32) stacked = tf.concat([stacked, lr], axis=-1) stacked = stacked[:,:,:,3:] print(stacked.shape) 1. Giving 10 frames for VSR by your tflite model which gives (720, 1280, 30) frames_10 = stacked[:,:,:,:30] vsr_model_path = './MAI-VSR-Diggers/ckpt/model_none.tflite' #vsr_model_path = './MAI-VSR-Diggers/ckpt/model.tflite' # Load TFLite model and allocate tensors. interpreter = tf.lite.Interpreter(model_path=vsr_model_path) # Get input and output tensors. input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() print(input_details, '\n',output_details) # Run the model interpreter.resize_tensor_input(input_details[0]['index'], [1, 180, 320, 30], strict=False) interpreter.allocate_tensors() interpreter.set_tensor(input_details[0]['index'], in_frames) interpreter.invoke() # Extract the output and postprocess it output_data = interpreter.get_tensor(output_details[0]['index']) vsr = tf.squeeze(output_data, axis=0) print(vsr.shape) 1. Display 1 frame of SR frame_1 = stacked[:,:,:,:3] #lr = tf.squeeze(frame_1, axis=0) lr = tf.cast(tf.squeeze(frame_1, axis=0), tf.uint8) print(lr.shape) #Image.fromarray(np.asarray(lr)).show() plt.figure(figsize = (5,6)) plt.title('LR') plt.imshow(lr.numpy()); tensor = vsr[:,:,:3] shape = tensor.shape image_scaled = minmax_scale(tf.reshape(tensor,shape=[-1]), feature_range=(0,255)).reshape(shape) tensor = tensor/255 print(tensor.shape) plt.figure(figsize=(25, 15)) plt.subplot(1, 2, 1) plt.title(f'VSR (x4)') plt.imshow(tensor.numpy()); bicubic = tf.image.resize(lr, [720, 1280], tf.image.ResizeMethod.BICUBIC) bicubic = tf.cast(bicubic, tf.uint8) plt.subplot(1, 2, 2) plt.title('Bicubic') plt.imshow(bicubic.numpy()); — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#2>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGC5WRETIPT5DJ4XTQKQI3TUPSNAVANCNFSM5JOMHNBA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.