Talking face extraction

batch_process.py

@@ -0,0 +1,67 @@
+import os
+import subprocess
+import argparse
+
+def main(args):
+    # Ensure the output directory exists
+    if not os.path.exists(args.videoFolderOutput):
+        os.makedirs(args.videoFolderOutput)
+
+    # List all videos in the input directory
+    video_files = [f for f in os.listdir(args.videoFolderInput) if f.endswith(('.mp4', '.avi', '.mov'))]
+    if not video_files:
+        print(f"No video files found in the directory: {args.videoFolderInput}")
+        return
+
+    # Process each video
+    for video_file in video_files:
+        video_name = os.path.splitext(video_file)[0]
+
+        output_video_path = args.videoFolderOutput
+
+        # Ensure output directory for the video exists
+        if not os.path.exists(output_video_path):
+            os.makedirs(output_video_path)
+
+        # Build the command to call demoTalkNet.py
+        command = [
+            "python", "demoTalkNet.py",
+            "--videoName", video_name,
+            "--videoFolderInput", args.videoFolderInput,
+            "--videoFolderOutput", args.videoFolderOutput,
+            "--channelName", args.channelName,
+        ]
+
+        # Print and execute the command
+        print(f"Processing video: {video_file}")
+        print("Command:", " ".join(command))
+        subprocess.run(command)
+
+    print("Batch processing completed.")
+
+if __name__ == "__main__":
+    # Parse arguments for the batch process
+    parser = argparse.ArgumentParser(description="Batch Process Videos with demoTalkNet")
+    parser.add_argument('--videoFolderInput', type=str, required=True, help='Path to the folder containing input videos.')
+    parser.add_argument('--videoFolderOutput', default="output_dir", type=str, help='Path to the folder for storing outputs and temporary files.')
+    parser.add_argument('--bucketName', type=str, help='Path to the folder for storing outputs and temporary files.')
+    parser.add_argument('--channelName', type=str, required=True, help='Path to the folder for storing outputs and temporary files.')
+    parser.add_argument('--pretrainModel', type=str,default="pretrain_TalkSet.model", help='Path to the pretrained TalkNet model.')
+    parser.add_argument('--fps', type=float, default=25, help='Desired FPS.')
+    parser.add_argument('--frame_size', type=int, default=512, help='Desired frame size.')
+    parser.add_argument('--angleThreshold', type=int, default=10, help='Yaw threshold.')
+    parser.add_argument('--contentDetectorThreshold', type=float, default=27.0, help='Content detector threshold.')
+    parser.add_argument('--thresholdDetectorThreshold', type=float, default=30.0, help='Threshold detector threshold.')
+    parser.add_argument('--nDataLoaderThread', type=int, default=10, help='Number of data loader threads.')
+    parser.add_argument('--facedetScale', type=float, default=0.25, help='Face detection scale factor.')
+    parser.add_argument('--minTrack', type=int, default=40, help='Minimum frames for each shot.')
+    parser.add_argument('--numFailedDet', type=int, default=5, help='Missed detections allowed before stopping tracking.')
+    parser.add_argument('--minFaceSize', type=int, default=100, help='Minimum face size in pixels.')
+    parser.add_argument('--cropScale', type=float, default=0.40, help='Scale bounding box.')
+    parser.add_argument('--start', type=int, default=0, help='Start time of the video.')
+    parser.add_argument('--duration', type=int, default=0, help='Duration of the video (0 for full video).')
+    parser.add_argument('--evalCol', action='store_true', help='Evaluate on Columbia dataset.')
+    parser.add_argument('--colSavePath', type=str, default="/data08/col", help='Path for inputs, temps, and outputs for Columbia evaluation.')
+
+    args = parser.parse_args()
+    main(args)

fileTransfer.py

@@ -0,0 +1,29 @@
+import boto3
+import os
+
+# Define the bucket name and local folder path
+bucket_name = "scenespart1"
+local_folder_path = "/home/ubuntu/ZeeNews"  # Folder to upload
+s3_object_key_prefix = "croppedData/ZeeNews_output/"  # S3 destination prefix
+
+# Create an S3 client
+s3_client = boto3.client("s3")
+
+def upload_folder_to_s3(local_folder, bucket, s3_prefix):
+    try:
+        for root, dirs, files in os.walk(local_folder):
+            for file in files:
+                local_file_path = os.path.join(root, file)
+                # Create the relative path for S3 object key
+                relative_path = os.path.relpath(local_file_path, local_folder)
+                s3_object_key = os.path.join(s3_prefix, relative_path)
+
+                # Upload the file
+                s3_client.upload_file(local_file_path, bucket, s3_object_key)
+                print(f"Uploaded {local_file_path} to s3://{bucket}/{s3_object_key}")
+
+    except Exception as e:
+        print(f"Error uploading folder to S3: {e}")
+
+# Call the function to upload the folder
+upload_folder_to_s3(local_folder_path, bucket_name, s3_object_key_prefix)

fileTransferFolder.py

@@ -0,0 +1,29 @@
+import boto3
+import os
+
+# Define the bucket name and local folder path
+bucket_name = "scenespart1"
+local_folder_path = "/home/ubuntu/ZeeNews"  # Folder to upload
+s3_object_key_prefix = "croppedData/ZeeNews_output/"  # S3 destination prefix
+
+# Create an S3 client
+s3_client = boto3.client("s3")
+
+def upload_folder_to_s3(local_folder, bucket, s3_prefix):
+    try:
+        for root, dirs, files in os.walk(local_folder):
+            for file in files:
+                local_file_path = os.path.join(root, file)
+                # Create the relative path for S3 object key
+                relative_path = os.path.relpath(local_file_path, local_folder)
+                s3_object_key = os.path.join(s3_prefix, relative_path)
+
+                # Upload the file
+                s3_client.upload_file(local_file_path, bucket, s3_object_key)
+                print(f"Uploaded {local_file_path} to s3://{bucket}/{s3_object_key}")
+
+    except Exception as e:
+        print(f"Error uploading folder to S3: {e}")
+
+# Call the function to upload the folder
+upload_folder_to_s3(local_folder_path, bucket_name, s3_object_key_prefix)

model/faceDetector/s3fd/box_utils.py

@@ -35,7 +35,7 @@ def nms_(dets, thresh):
         inds = np.where(ovr <= thresh)[0]
         order = order[inds + 1]
 
-    return np.array(keep).astype(np.int)
+    return np.array(keep).astype(int)
 
 
 def decode(loc, priors, variances):

requirement.txt

@@ -10,4 +10,5 @@ python_speech_features
 torchvision
 ffmpeg
 gdown
-youtube-dl
+youtube-dl
+mediapipe==0.10.9

s3_uploader.py

@@ -0,0 +1,19 @@
+import boto3
+
+def upload_file_to_s3(local_file, bucket, s3_key):
+    """
+    Upload a single file to an S3 bucket.
+
+    :param local_file: Path to the local file.
+    :param bucket: Name of the S3 bucket.
+    :param s3_key: Path and name of the file in the S3 bucket.
+    """
+    # Create an S3 client
+    s3_client = boto3.client("s3")
+
+    try:
+        # Upload the file
+        s3_client.upload_file(local_file, bucket, s3_key)
+        print(f"Uploaded {local_file} to s3://{bucket}/{s3_key}")
+    except Exception as e:
+        print(f"Error uploading file: {e}")