Update app.py

app.py

@@ -8,13 +8,14 @@ from gtts import gTTS
 import tempfile
 from pydub.generators import Sine
 from pydub import AudioSegment
+import dlib
 import cv2
 import imageio
+import os
 import ffmpeg
 from io import BytesIO
 import requests
 import sys
-import mediapipe as mp
 
 python_path = sys.executable
 
@@ -23,35 +24,56 @@ from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
 
 block = gr.Blocks()
 
-def crop_src_image(src_img):
-    mp_face_detection = mp.solutions.face_detection
-    mp_drawing = mp.solutions.drawing_utils
-    
-    save_img = '/content/image_pre.png'
+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
-    
-    with mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5) as face_detection:
-        results = face_detection.process(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
-        if results.detections:
-            detection = results.detections[0]  # Use the first detected face
-            bboxC = detection.location_data.relative_bounding_box
-            x = int(bboxC.xmin * width)
-            y = int(bboxC.ymin * h)
-            w = int(bboxC.width * width)
-            h = int(bboxC.height * h)
-            
-            # Ensure bbox dimensions are within image boundaries
-            x, y = max(0, x), max(0, y)
-            w, h = min(width - x, w), min(h - y, h)
-            
-            img = img[y:y + h, x:x + w]
-            img = cv2.resize(img, (256, 256))
-            cv2.imwrite(save_img, img)
-        else:
-            # If no face is detected, resize the original image
-            img = cv2.resize(img, (256, 256))
-            cv2.imwrite(save_img, img)
+    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)
     return save_img
 
 def pad_image(image):
@@ -84,108 +106,153 @@ def calculate(image_in, audio_in):
         image = Image.open(BytesIO(response.content))
     print("****"*100)
     image = pad_image(image)
+    # os.system(f"rm -rf /content/image.png")
     image.save("image.png")
 
     pocketsphinx_run = subprocess.run(['pocketsphinx', '-phone_align', 'yes', 'single', '/content/audio.wav'], check=True, capture_output=True)
     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"rm -rf /content/image_audio.mp4")
     os.system(f"cd /content/one-shot-talking-face && {python_path} -B test_script.py --img_path /content/image.png --audio_path /content/audio.wav --phoneme_path /content/test.json --save_dir /content/train")
     return "/content/train/image_audio.mp4"
 
 def merge_frames():
-    path = '/content/video_results/restored_imgs'
+
+
+  path = '/content/video_results/restored_imgs'
     
-    if not os.path.exists(path):
-        os.makedirs(path)
+  if not os.path.exists(path):
+      os.makedirs(path)
         
-    image_folder = os.fsencode(path)
-    filenames = []
+  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)
+  for file in os.listdir(image_folder):
+      filename = os.fsdecode(file)
+      if filename.endswith( ('.jpg', '.png', '.gif') ):
+          filenames.append(filename)
 
-    filenames.sort()
-    images = list(map(lambda filename: imageio.imread("/content/video_results/restored_imgs/" + filename), filenames))
-    imageio.mimsave('/content/video_output.mp4', images, fps=25.0)
-    return "/content/video_output.mp4"
+  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))
+  # os.system(f"rm -rf /content/video_output.mp4")
+  imageio.mimsave('/content/video_output.mp4', images, fps=25.0) # modify the frame duration as needed
+  return "/content/video_output.mp4"
 
 def audio_video():
-    input_video = ffmpeg.input('/content/video_output.mp4')
-    input_audio = ffmpeg.input('/content/audio.wav')
-    os.system(f"rm -rf /content/final_output.mp4")
-    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):
-    crop_img = crop_src_image(image_in)
+  input_video = ffmpeg.input('/content/video_output.mp4')
 
-    if os.path.exists("/content/results/restored_imgs/image_pre.png"):
-        os.system(f"rm -rf /content/results/restored_imgs/image_pre.png")
+  input_audio = ffmpeg.input('/content/audio.wav')
+  os.system(f"rm -rf /content/final_output.mp4")
+  ffmpeg.concat(input_video, input_audio, v=1, a=1).output('/content/final_output.mp4').run()
   
-    if not os.path.exists("/content/results"):
-        os.makedirs("/content/results")
+  return "/content/final_output.mp4"
+
+def one_shot_talking(image_in,audio_in):
+
+
+  # Pre-processing of image
+  crop_img=crop_src_image(image_in)
+
+  if os.path.exists("/content/results/restored_imgs/image_pre.png"):
+    os.system(f"rm -rf /content/results/restored_imgs/image_pre.png")
+  
+  if not os.path.exists( "/content/results" ):
+    os.makedirs("/content/results")
   
-    os.system(f"{python_path} /content/GFPGAN/inference_gfpgan.py --upscale 2 -i /content/image_pre.png -o /content/results --bg_upsampler realesrgan")
-    image_in_one_shot = '/content/results/image_pre.png'
+  #Improve quality of input image
+  os.system(f"{python_path} /content/GFPGAN/inference_gfpgan.py --upscale 2 -i /content/image_pre.png -o /content/results --bg_upsampler realesrgan")
+  # time.sleep(60)
   
-    calculate(image_in_one_shot, audio_in)
-    os.system(f"{python_path} /content/PyVideoFramesExtractor/extract.py --video=/content/train/image_audio.mp4")
-    os.system(f"rm -rf /content/video_results/")
-    os.system(f"{python_path} /content/GFPGAN/inference_gfpgan.py --upscale 2 -i /content/extracted_frames/image_audio_frames -o /content/video_results --bg_upsampler realesrgan")
-    merge_frames()
-    return audio_video()
-
-def one_shot(image_in, input_text, gender):
+  image_in_one_shot='/content/results/image_pre.png'
+  
+  #One Shot Talking Face algorithm
+  calculate(image_in_one_shot,audio_in)
+
+  #Video Quality Improvement
+  os.system(f"rm -rf /content/extracted_frames/image_audio_frames")
+  #1. Extract the frames from the video file using PyVideoFramesExtractor
+  os.system(f"{python_path} /content/PyVideoFramesExtractor/extract.py --video=/content/train/image_audio.mp4")
+
+  #2. Improve image quality using GFPGAN on each frames
+  # os.system(f"rm -rf /content/extracted_frames/image_audio_frames")
+  os.system(f"rm -rf /content/video_results/")
+  os.system(f"{python_path} /content/GFPGAN/inference_gfpgan.py --upscale 2 -i /content/extracted_frames/image_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_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")
-            os.system(f"rm -rf /content/audio.wav")
-            sound.export("/content/audio.wav", format="wav")
-            audio_in = "/content/audio.wav"
-        return one_shot_talking(image_in, audio_in)
+         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")
+              os.system(f"rm -rf /content/audio.wav")
+              sound.export("/content/audio.wav", format="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]
-        TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
-        generator = task.build_generator([model], cfg)
-
-        sample = TTSHubInterface.get_model_input(task, input_text)
-        sample["net_input"]["src_tokens"] = sample["net_input"]["src_tokens"]
-        sample["net_input"]["src_lengths"] = sample["net_input"]["src_lengths"]
-        sample["speaker"] = sample["speaker"]
-
-        wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
-        os.system(f"rm -rf /content/audio_before.wav")
-        soundfile.write("/content/audio_before.wav", wav.cpu().clone().numpy(), rate)
-        os.system(f"rm -rf /content/audio.wav")
-        cmd = 'ffmpeg -i /content/audio_before.wav -filter:a "atempo=0.7" -vn /content/audio.wav'
-        os.system(cmd)
-        audio_in = "/content/audio.wav"
-        return one_shot_talking(image_in, audio_in)
+       
+          models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
+              "Voicemod/fastspeech2-en-male1",
+              arg_overrides={"vocoder": "hifigan", "fp16": False}
+          )
+
+          model = models[0]
+          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"]
+          sample["net_input"]["src_lengths"] = sample["net_input"]["src_lengths"]
+          sample["speaker"] = sample["speaker"]
+
+          wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
+          # soundfile.write("/content/audio_before.wav", wav, rate)
+          os.system(f"rm -rf /content/audio_before.wav")
+          soundfile.write("/content/audio_before.wav", wav.cpu().clone().numpy(), rate)
+          os.system(f"rm -rf /content/audio.wav")
+          cmd='ffmpeg -i /content/audio_before.wav -filter:a "atempo=0.7" -vn /content/audio.wav'
+          os.system(cmd)
+          audio_in="/content/audio.wav"
+          
+          return one_shot_talking(image_in,audio_in)
+           
+                        
 def run():
-    with gr.Blocks(css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}") as demo:
-        gr.Markdown("<h1 style='text-align: center;'>One Shot Talking Face from Text</h1><br/><br/>")
-        with gr.Group():
-            with gr.Row():
-                image_in = gr.Image(show_label=True, type="filepath", label="Input Image")
-                input_text = gr.Textbox(show_label=True, label="Input Text")
-                gender = gr.Radio(["Female", "Male"], value="Female", label="Gender")
-                video_out = gr.Video(show_label=True, label="Output")
-            with gr.Row():
-                btn = gr.Button("Generate")   
-        btn.click(one_shot, inputs=[image_in, input_text, gender], outputs=[video_out])
-        demo.queue()
-        demo.launch(server_name="0.0.0.0", server_port=7860)
+  with gr.Blocks(css=".gradio-container {background-color: lightgray} #radio_div {background-color: #FFD8B4; font-size: 40px;}") as demo:
+    gr.Markdown("<h1 style='text-align: center;'>"+ "One Shot Talking Face from Text" + "</h1><br/><br/>")
+    with gr.Group():
+      # with gr.Box():
+        with gr.Row():
+        # with gr.Row().style(equal_height=True):
+          image_in = gr.Image(show_label=True, type="filepath",label="Input Image")
+          input_text = gr.Textbox(show_label=True,label="Input Text")
+          gender = gr.Radio(["Female","Male"],value="Female",label="Gender")
+          video_out = gr.Video(show_label=True,label="Output")
+        with gr.Row():
+        # with gr.Row().style(equal_height=True):
+          btn = gr.Button("Generate")   
+    # gr.Markdown(
+    #         """
+    #         <p style='text-align: center;'>Feel free to give us your thoughts on this demo and please contact us at 
+    #                 <a href="mailto:[email protected]" target="_blank">[email protected]</a> 
+    #                 <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>
+
+    #         """)
+
+    btn.click(one_shot, inputs=[image_in,input_text,gender], outputs=[video_out])
+    demo.queue()
+    demo.launch(server_name="0.0.0.0", server_port=7860)
 
 if __name__ == "__main__":
     run()