Handled cases when no landmark in some of the frame in videos.

.gitignore

@@ -22,5 +22,10 @@ libreface/Facial_Expression_Recognition/weights/
 weights_libreface/
 build/
 dist/
+temp/
+tmp/
+test_instruction.txt
+test_issue11.py
+/data
 *.egg-info/*
 *.DS_Store

README_pypi.rst

@@ -13,7 +13,7 @@ Libreface
    :alt: Static Badge
 
 
-.. |badge2| image:: https://img.shields.io/badge/python-%3D%3D3.8-green
+.. |badge2| image:: https://img.shields.io/badge/python-%3D%3D3.9-green
    :alt: Static Badge
 
 
@@ -29,7 +29,7 @@ We conduct extensive experiments of pre-training and distillation to demonstrate
 Dependencies
 ============
 
-- Python 3.8
+- Python 3.9
 - You should have `cmake` installed in your system.
     - **For Linux users** - :code:`sudo apt-get install cmake`. If you run into trouble, consider upgrading to the latest version (`instructions`_).
     - **For Mac users** - :code:`brew install cmake`.
@@ -39,11 +39,11 @@ Dependencies
 
 Installation
 ============
-You can first create a new Python 3.8 environment using `conda` and then install this package using `pip` from the PyPI hub:
+You can first create a new Python 3.9 environment using `conda` and then install this package using `pip` from the PyPI hub:
 
 .. code-block:: bash
 
-    conda create -n libreface_env python=3.8
+    conda create -n libreface_env python=3.9
     conda activate libreface_env
     pip install --upgrade libreface
 

libreface/__init__.py

@@ -68,6 +68,8 @@ def get_facial_attributes_video(video_path,
     cur_video_name = ".".join(video_path.split("/")[-1].split(".")[:-1])
     aligned_frames_path_list, headpose_list, landmarks_3d_list = get_aligned_video_frames(frames_df, temp_dir=os.path.join(temp_dir, cur_video_name))
     # frames_df["aligned_frame_path"] = aligned_frames_path_list
+    if frames_df.empty:
+        raise RuntimeError(f"No face detected in the video, get_facial_attributes fails.")
     frames_df = frames_df.drop("path_to_frame", axis=1)
     frames_df["headpose"] = headpose_list
     frames_df["landmarks_3d"] = landmarks_3d_list
@@ -118,13 +120,19 @@ def save_facial_attributes_video(video_path,
                             batch_size = 256,
                             num_workers = 2,
                             weights_download_dir:str = "./weights_libreface"):
-    frames_df = get_facial_attributes_video(video_path,
-                                            model_choice=model_choice,
-                                            temp_dir=temp_dir,
-                                            device=device, 
-                                            batch_size=batch_size,
-                                            num_workers=num_workers,
-                                            weights_download_dir=weights_download_dir)
+    try:
+        frames_df = get_facial_attributes_video(video_path,
+                                                model_choice=model_choice,
+                                                temp_dir=temp_dir,
+                                                device=device, 
+                                                batch_size=batch_size,
+                                                num_workers=num_workers,
+                                                weights_download_dir=weights_download_dir)
+
+    except Exception as e:
+        print(e)
+        print(f"Since no face is detected, can't save the file")
+        return False
     save_path = uniquify_file(output_save_path)
     frames_df.to_csv(save_path, index=False)
     print(f"Facial attributes of the video saved to {save_path}")

libreface/detect_mediapipe_image.py

@@ -205,7 +205,8 @@ def get_aligned_image(image_path, temp_dir = "./tmp", verbose=False):
       # Draw face landmarks of each face.
       # print(f'Face landmarks of {name}:')
       if not results.multi_face_landmarks:
-        print("Processing landmarks did not result on anything...")
+        raise RuntimeError(f"No face landmarks")
+        # print("Processing landmarks did not result on anything...")
       img_h, img_w, img_c = image.shape
       face_3d = []
       face_2d = []
@@ -348,12 +349,24 @@ def get_aligned_video_frames(frames_df, temp_dir="./tmp"):
   aligned_frames_paths = []
   head_pose_list = []
   landmark_list = []
-  for _, row in tqdm(frames_df.iterrows(), desc="Aligning face for video frames..."):
-      aligned_image_path, head_pose, landmark_dict = get_aligned_image(row["path_to_frame"], temp_dir)
+   #track the errored frames
+  indexes_to_drop = []
+  for index, row in tqdm(frames_df.iterrows(), desc="Aligning face for video frames..."):
+      try:
+        aligned_image_path, head_pose, landmark_dict = get_aligned_image(row["path_to_frame"], temp_dir)
+      except Exception as e:
+        indexes_to_drop.append(index) 
+        continue
       aligned_frames_paths.append(aligned_image_path)
       head_pose_list.append(head_pose)
       landmark_list.append(landmark_dict)
-
+  #drop all the errored frames
+  frames_df.drop(index=indexes_to_drop,inplace=True)
+  frames_df.reset_index(drop=True, inplace=True)
+  if len(indexes_to_drop)!=0 :
+    print(f"Dropped {len(indexes_to_drop)} frames because no landmarks detected by mediapipe in the frame.")
+  if frames_df.empty:
+    print("No face detected in the provided video")
   return aligned_frames_paths, head_pose_list, landmark_list
 
 if __name__ == "__main__":

requirements_new.txt

@@ -5,7 +5,7 @@ gdown==5.2.0
 importlib-metadata==8.4.0
 importlib-resources==6.4.5
 imutils==0.5.4
-mediapipe==0.9.3
+mediapipe==0.10.5
 oauthlib>=3.0.0
 opencv-python==4.10.0.84
 pandas==2.0.3

setup.py

@@ -14,7 +14,7 @@
 URL = 'https://boese0601.github.io/libreface'
 EMAIL = '[email protected]'
 AUTHOR = 'IHP-Lab'
-REQUIRES_PYTHON = '>=3.8'
+REQUIRES_PYTHON = '>=3.9'
 
 
 # What packages are required for this module to be executed?
@@ -52,15 +52,15 @@ def list_reqs(fname='requirements_new.txt'):
 # with open(PACKAGE_DIR / 'VERSION') as f:
 #     _version = f.read().strip()
 
-about['__version__'] = "0.0.19"
+about['__version__'] = "0.0.26"
 
 
 # Where the magic happens:
 setup(
     name=NAME,
     version=about['__version__'],
     description='LibreFace model for facial analysis',
-    long_descripation_content_type='text/x-rst',
+    long_description_content_type='text/x-rst',
     long_description=long_description,
     author=AUTHOR,
     author_email=EMAIL,
@@ -73,14 +73,16 @@ def list_reqs(fname='requirements_new.txt'):
     include_package_data=True,
     license=license,
     entry_points={
-        'console_scripts': 'libreface=libreface.commandline:main_func'
+        'console_scripts': [
+            'libreface=libreface.commandline:main_func'
+        ]
     },
     classifiers=[
         # Trove classifiers
         # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
         'Programming Language :: Python',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: Implementation :: CPython',
         'Programming Language :: Python :: Implementation :: PyPy'
     ],

test_libreface.py

@@ -2,29 +2,29 @@
 import time
 
 # inference on single image and store results to a variable
-detected_facial_attributes = libreface.get_facial_attributes_image(image_path = "sample_disfa.png",
+detected_facial_attributes = libreface.get_facial_attributes_image(image_path = "examples/sample_disfa.png",
                                                                    temp_dir = "./temp",
                                                                    device = "cpu")
 
 # inference on a single image and save results in a csv file
-libreface.save_facial_attributes_image(image_path = "sample_disfa.png",
+libreface.save_facial_attributes_image(image_path = "examples/sample_disfa.png",
                                        output_save_path = "sample_image_results.csv",
                                        temp_dir = "./temp",
                                        device = "cpu")
 
 # inference on a video and store the results to a pandas dataframe
-detected_facial_attributes_df = libreface.get_facial_attributes_video(video_path = "sample_disfa.avi",
+detected_facial_attributes_df = libreface.get_facial_attributes_video(video_path = "examples/sample_disfa.avi",
                                                                       temp_dir = "./temp",
                                                                       device = "cpu")
 
 # ## inference on a video and save the results framewise in a csv file
-libreface.save_facial_attributes_video(video_path = "sample_disfa.avi",
+libreface.save_facial_attributes_video(video_path = "examples/sample_disfa.avi",
                                        output_save_path = "sample_video_results.csv",
                                        temp_dir = "./temp",
                                        device = "cpu")
 
 ## inference on any image or video type and store results accordingly to a variable or save results
-detected_facial_attributes = libreface.get_facial_attributes(file_path = "sample_disfa.avi",
+detected_facial_attributes = libreface.get_facial_attributes(file_path = "examples/sample_disfa.avi",
                                                              output_save_path = "sample_results.csv",
                                                              temp_dir = "./temp",
                                                              device = "cpu")