Update app.py

app.py

@@ -11,10 +11,67 @@ from gtts import gTTS
 import tempfile
 from pydub import AudioSegment
 from pydub.generators import Sine
+from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
+from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
+import dlib
+import cv2
+import imageio
+import ffmpeg
 
 
 block = gr.Blocks()
 
+def compute_aspect_preserved_bbox(bbox, increase_area, h, w):
+    left, top, right, bot = bbox
+    width = right - left
+    height = bot - top
+
+    width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
+    height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
+
+    left_t = int(left - width_increase * width)
+    top_t = int(top - height_increase * height)
+    right_t = int(right + width_increase * width)
+    bot_t = int(bot + height_increase * height)
+
+    left_oob = -min(0, left_t)
+    right_oob = right - min(right_t, w)
+    top_oob = -min(0, top_t)
+    bot_oob = bot - min(bot_t, h)
+
+    if max(left_oob, right_oob, top_oob, bot_oob) > 0:
+        max_w = max(left_oob, right_oob)
+        max_h = max(top_oob, bot_oob)
+        if max_w > max_h:
+            return left_t + max_w, top_t + max_w, right_t - max_w, bot_t - max_w
+        else:
+            return left_t + max_h, top_t + max_h, right_t - max_h, bot_t - max_h
+
+    else:
+        return (left_t, top_t, right_t, bot_t)
+
+def crop_src_image(src_img, detector=None):
+    if  detector is None:
+        detector = dlib.get_frontal_face_detector()
+    save_img='/content/image_pre.png'
+    img = cv2.imread(src_img)
+    faces = detector(img, 0)
+    h, width, _ = img.shape
+    if len(faces) > 0:
+        bbox = [faces[0].left(), faces[0].top(),faces[0].right(), faces[0].bottom()]
+        l = bbox[3]-bbox[1]
+        bbox[1]= bbox[1]-l*0.1
+        bbox[3]= bbox[3]-l*0.1
+        bbox[1] = max(0,bbox[1])
+        bbox[3] = min(h,bbox[3])
+        bbox = compute_aspect_preserved_bbox(tuple(bbox), 0.5, img.shape[0], img.shape[1])
+        img = img[bbox[1] :bbox[3] , bbox[0]:bbox[2]]
+        img = cv2.resize(img, (256, 256))
+        cv2.imwrite(save_img,img)
+    else:
+        img = cv2.resize(img,(256,256))
+        cv2.imwrite(save_img, img)
+
 def pad_image(image):
     w, h = image.size
     if w == h:
@@ -30,7 +87,6 @@ def pad_image(image):
 
 def calculate(image_in, audio_in):
     waveform, sample_rate = torchaudio.load(audio_in)
-    waveform = torch.mean(waveform, dim=0, keepdim=True)
     torchaudio.save("/content/audio.wav", waveform, sample_rate, encoding="PCM_S", bits_per_sample=16)
     image = Image.open(image_in)
     image = pad_image(image)
@@ -40,39 +96,141 @@ def calculate(image_in, audio_in):
     jq_run = subprocess.run(['jq', '[.w[]|{word: (.t | ascii_upcase | sub("<S>"; "sil") | sub("<SIL>"; "sil") | sub("\\\(2\\\)"; "") | sub("\\\(3\\\)"; "") | sub("\\\(4\\\)"; "") | sub("\\\[SPEECH\\\]"; "SIL") | sub("\\\[NOISE\\\]"; "SIL")), phones: [.w[]|{ph: .t | sub("\\\+SPN\\\+"; "SIL") | sub("\\\+NSN\\\+"; "SIL"), bg: (.b*100)|floor, ed: (.b*100+.d*100)|floor}]}]'], input=pocketsphinx_run.stdout, capture_output=True)
     with open("test.json", "w") as f:
         f.write(jq_run.stdout.decode('utf-8').strip())
-    # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    os.system(f"cd /content/one-shot-talking-face && python3 -B test_script.py --img_path /content/image.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
+
+    os.system(f"cd /content/one-shot-talking-face && python3 -B test_script.py --img_path /content/results/restored_imgs/image_pre.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
     return "/content/train/image_audio.mp4"
+    
+
+def merge_frames():
+  import imageio
+  import os
+
+  path = '/content/video_results/restored_imgs'
+  image_folder = os.fsencode(path)
+  print(image_folder)
+  filenames = []
+
+  for file in os.listdir(image_folder):
+      filename = os.fsdecode(file)
+      if filename.endswith( ('.jpg', '.png', '.gif') ):
+          filenames.append(filename)
+
+  filenames.sort() # this iteration technique has no built in order, so sort the frames
+  print(filenames)
+  images = list(map(lambda filename: imageio.imread("/content/video_results/restored_imgs/"+filename), filenames))
+
+  
+  imageio.mimsave('/content/video_output.mp4', images, fps=25.0) # modify the frame duration as needed
+
+
+
+def audio_video():
+
+  input_video = ffmpeg.input('/content/video_output.mp4')
+
+  input_audio = ffmpeg.input('/content/audio.wav')
+
+  ffmpeg.concat(input_video, input_audio, v=1, a=1).output('/content/final_output.mp4').run()
+    
+  return "/content/final_output.mp4"
+
+def one_shot_talking(image_in,audio_in):
+
+
+  #Pre-processing of image
+  crop_src_image(image_in)
+
+  #Improve quality of input image
+  os.system(python /content/GFPGAN/inference_gfpgan.py --upscale 2 -i /content/image_pre.png -o /content/results  --bg_upsampler realesrgan)
 
-def one_shot(image_in,input_text,gender):
-    if gender == "Female":
-         tts = gTTS(input_text)
-         with tempfile.NamedTemporaryFile(suffix='.mp3', delete=False) as f:
-              tts.write_to_fp(f)
-              f.seek(0)
-              sound = AudioSegment.from_file(f.name, format="mp3")
-              sound.export("/content/audio.wav", format="wav")
-              audio_in="/content/audio.wav"
-         return calculate(image_in,audio_in)
-                        
+  image_in_one_shot='/content/results/restored_imgs/image_pre.png'
+
+  #One Shot Talking Face algorithm
+  calculate(image_in_one_shot,audio_in)
+
+  #Video Quality Improvement
+
+  #1. Extract the frames from the video file using PyVideoFramesExtractor
+  os.system(python /content/PyVideoFramesExtractor/extract.py --video=/content/train/image_pre_audio.mp4)
+
+  #2. Improve image quality using GFPGAN on each frames
+  os.system(python /content/GFPGAN/inference_gfpgan.py --upscale 2 -i /content/extracted_frames/image_pre_audio_frames -o /content/video_results  --bg_upsampler realesrgan)
+
+  #3. Merge all the frames to a one video using imageio
+  merge_frames()
+
+  return audio_video()
+
+
+
+
+    
+def one_shot(image,input_text,gender): 
+   if gender == "Female":
+     tts = gTTS(input_text)
+     with tempfile.NamedTemporaryFile(suffix='.mp3', delete=False) as f:
+          tts.write_to_fp(f)
+          f.seek(0)
+          sound = AudioSegment.from_file(f.name, format="mp3")
+          sound.export("/content/audio.wav", format="wav")
+     waveform, sample_rate = torchaudio.load("/content/audio.wav")
+     audio_in="/content/audio.wav"
+
+     return one_shot_talking(image_in,audio_in)
+   elif gender == "Male":
+
+      models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
+          "Voicemod/fastspeech2-en-male1",
+          arg_overrides={"vocoder": "hifigan", "fp16": False}
+      )
+
+      model = models[0].cuda()
+      TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
+      generator = task.build_generator([model], cfg)
+      # next(model.parameters()).device
+
+      sample = TTSHubInterface.get_model_input(task, input_text)
+      sample["net_input"]["src_tokens"] = sample["net_input"]["src_tokens"].cuda()
+      sample["net_input"]["src_lengths"] = sample["net_input"]["src_lengths"].cuda()
+      sample["speaker"] = sample["speaker"].cuda()
+
+      wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
+      # soundfile.write("/content/audio_before.wav", wav, rate)
+      soundfile.write("/content/audio_before.wav", wav.cpu().clone().numpy(), rate)
+      cmd='ffmpeg -i /content/audio_before.wav -filter:a "atempo=0.7" -vn /content/audio.wav'
+      os.system(cmd)
+      return one_shot_talking(image,"/content/audio.wav")
+
+       
+
+
+def generate_ocr(method,image,gender):
+    return "Hello"
+    
 def run():
   with block:
-    
+  
     with gr.Group():
       with gr.Box():
         with gr.Row().style(equal_height=True):
           image_in = gr.Image(show_label=False, type="filepath")
-          input_text = gr.Textbox(show_label=False)
+          # audio_in = gr.Audio(show_label=False, type='filepath')
+          input_text=gr.Textbox(lines=3, value="Hello How are you?", label="Input Text")
           gender = gr.Radio(["Female","Male"],value="Female",label="Gender")
-          video_out = gr.Video(show_label=False)
+          video_out = gr.Textbox(label="output")
+          # video_out = gr.Video(show_label=False)
         with gr.Row().style(equal_height=True):
           btn = gr.Button("Generate")          
 
-    
-    btn.click(one_shot, inputs=[image_in,input_text,gender], outputs=[video_out])
-    block.queue()
+    btn.click(one_shot, inputs=[image_in, input_text,gender], outputs=[video_out])
+    # block.queue()
     block.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
     run()
 
+
+
+
+
+