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Deep-Live-Cam
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base: diaojiaolou:166d5a34e270550e6e10aa0e7a3cbdadc7db6804
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diaojiaolou:main
diaojiaolou:refactoring
diaojiaolou:testing
diaojiaolou:privacy
diaojiaolou:pr/894
diaojiaolou:UI
diaojiaolou:experimental
diaojiaolou:2.0
diaojiaolou:1.8
diaojiaolou:1.7
diaojiaolou:Deep-Live-Cam
diaojiaolou:Gumroad
diaojiaolou:Deep_Live_Cam_CUDA
..
compare: diaojiaolou:5f2e54552cadc29fa96b6119fbf9636dfcdd4521
Branches Tags
diaojiaolou:main
diaojiaolou:refactoring
diaojiaolou:testing
diaojiaolou:privacy
diaojiaolou:pr/894
diaojiaolou:UI
diaojiaolou:experimental
diaojiaolou:2.0
diaojiaolou:1.8
diaojiaolou:1.7
diaojiaolou:Deep-Live-Cam
diaojiaolou:Gumroad
diaojiaolou:Deep_Live_Cam_CUDA

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6
README.md
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@ -178,7 +178,7 @@ It is highly recommended to use Python 3.10 for Windows for best compatibility w
* **Visual Studio Runtimes:** If you encounter errors during `pip install` for packages that compile C code (e.g., some scientific computing or image processing libraries), you might need the [Visual Studio Build Tools (or Runtimes)](https://visualstudio.microsoft.com/visual-cpp-build-tools/). Ensure "C++ build tools" (or similar workload) are selected during installation. * **Visual Studio Runtimes:** If you encounter errors during `pip install` for packages that compile C code (e.g., some scientific computing or image processing libraries), you might need the [Visual Studio Build Tools (or Runtimes)](https://visualstudio.microsoft.com/visual-cpp-build-tools/). Ensure "C++ build tools" (or similar workload) are selected during installation.
* **Virtual Environment (Manual Alternative):** If you prefer to set up the virtual environment manually instead of using `setup_windows.bat`: * **Virtual Environment (Manual Alternative):** If you prefer to set up the virtual environment manually instead of using `setup_windows.bat`:
```batch ```batch
python -m venv .venv python -m venv .venv
.venv\Scripts\activate.bat .venv\Scripts\activate.bat
python -m pip install --upgrade pip python -m pip install --upgrade pip
python -m pip install -r requirements.txt python -m pip install -r requirements.txt
@ -244,10 +244,10 @@ For a streamlined setup on macOS, use the provided shell scripts:
# Ensure you are using Python 3.10 if required by your chosen onnxruntime-silicon version # Ensure you are using Python 3.10 if required by your chosen onnxruntime-silicon version
# After running setup_mac.sh and activating .venv: # After running setup_mac.sh and activating .venv:
# source .venv/bin/activate # source .venv/bin/activate
pip uninstall onnxruntime onnxruntime-gpu # Uninstall any existing onnxruntime pip uninstall onnxruntime onnxruntime-gpu # Uninstall any existing onnxruntime
pip install onnxruntime-silicon==1.13.1 # Or your desired version pip install onnxruntime-silicon==1.13.1 # Or your desired version
# Then use ./run_mac_coreml.sh # Then use ./run_mac_coreml.sh
``` ```
Check the ONNX Runtime documentation for the latest recommended packages for Apple Silicon. Check the ONNX Runtime documentation for the latest recommended packages for Apple Silicon.

51
modules/hair_segmenter.py
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@ -26,49 +26,34 @@ def segment_hair(image_np: np.ndarray) -> np.ndarray:
try: try:
HAIR_SEGMENTER_PROCESSOR = SegformerImageProcessor.from_pretrained(MODEL_NAME) HAIR_SEGMENTER_PROCESSOR = SegformerImageProcessor.from_pretrained(MODEL_NAME)
HAIR_SEGMENTER_MODEL = SegformerForSemanticSegmentation.from_pretrained(MODEL_NAME) HAIR_SEGMENTER_MODEL = SegformerForSemanticSegmentation.from_pretrained(MODEL_NAME)
# Optional: Move model to GPU if available and if other models use GPU
if torch.cuda.is_available(): # if torch.cuda.is_available():
try: # HAIR_SEGMENTER_MODEL = HAIR_SEGMENTER_MODEL.to('cuda')
HAIR_SEGMENTER_MODEL = HAIR_SEGMENTER_MODEL.to('cuda') # print("Hair segmentation model moved to GPU.")
print("INFO: Hair segmentation model moved to CUDA (GPU).") print("Hair segmentation model and processor loaded successfully.")
except Exception as e_cuda:
print(f"ERROR: Failed to move hair segmentation model to CUDA: {e_cuda}. Using CPU instead.")
# Fallback to CPU if .to('cuda') fails
HAIR_SEGMENTER_MODEL = HAIR_SEGMENTER_MODEL.to('cpu')
else:
print("INFO: CUDA not available. Hair segmentation model will use CPU.")
print("INFO: Hair segmentation model and processor loaded successfully (device: {}).".format(HAIR_SEGMENTER_MODEL.device))
except Exception as e: except Exception as e:
print(f"ERROR: Failed to load hair segmentation model/processor: {e}") print(f"Failed to load hair segmentation model/processor: {e}")
# Return an empty mask compatible with expected output shape (H, W) # Return an empty mask compatible with expected output shape (H, W)
return np.zeros((image_np.shape[0], image_np.shape[1]), dtype=np.uint8) return np.zeros((image_np.shape[0], image_np.shape[1]), dtype=np.uint8)
# Ensure processor and model are loaded before proceeding
if HAIR_SEGMENTER_PROCESSOR is None or HAIR_SEGMENTER_MODEL is None:
print("Error: Hair segmentation models are not available.")
return np.zeros((image_np.shape[0], image_np.shape[1]), dtype=np.uint8)
# Convert BGR (OpenCV) to RGB (PIL) # Convert BGR (OpenCV) to RGB (PIL)
image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) image_rgb = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
image_pil = Image.fromarray(image_rgb) image_pil = Image.fromarray(image_rgb)
inputs = HAIR_SEGMENTER_PROCESSOR(images=image_pil, return_tensors="pt") inputs = HAIR_SEGMENTER_PROCESSOR(images=image_pil, return_tensors="pt")
if HAIR_SEGMENTER_MODEL.device.type == 'cuda': # Optional: Move inputs to GPU if model is on GPU
try: # if HAIR_SEGMENTER_MODEL.device.type == 'cuda':
# SegformerImageProcessor output (BatchEncoding) is a dict-like object. # inputs = inputs.to(HAIR_SEGMENTER_MODEL.device)
# We need to move its tensor components, commonly 'pixel_values'.
if 'pixel_values' in inputs:
inputs['pixel_values'] = inputs['pixel_values'].to('cuda')
else: # Fallback if the structure is different than expected
inputs = inputs.to('cuda')
# If inputs has other tensor components that need to be moved, they'd need similar handling.
except Exception as e_inputs_cuda:
print(f"ERROR: Failed to move inputs to CUDA: {e_inputs_cuda}. Attempting inference on CPU.")
# If moving inputs to CUDA fails, we should ensure model is also on CPU for this inference pass
# This is a tricky situation; ideally, this failure shouldn't happen if model moved successfully.
# For simplicity, we'll assume if model is on CUDA, inputs should also be.
# A more robust solution might involve moving model back to CPU if inputs can't be moved.
with torch.no_grad(): # Important for inference with torch.no_grad(): # Important for inference
outputs = HAIR_SEGMENTER_MODEL(**inputs) outputs = HAIR_SEGMENTER_MODEL(**inputs)
logits = outputs.logits # Shape: batch_size, num_labels, height, width logits = outputs.logits # Shape: batch_size, num_labels, height, width
# Upsample logits to original image size # Upsample logits to original image size
@ -100,7 +85,7 @@ if __name__ == '__main__':
# Create a dummy image for a basic test run if no image is available. # Create a dummy image for a basic test run if no image is available.
dummy_image_np = np.zeros((100, 100, 3), dtype=np.uint8) # 100x100 BGR image dummy_image_np = np.zeros((100, 100, 3), dtype=np.uint8) # 100x100 BGR image
dummy_image_np[:, :, 1] = 255 # Make it green to distinguish from black mask dummy_image_np[:, :, 1] = 255 # Make it green to distinguish from black mask
try: try:
print("Running segment_hair with a dummy image...") print("Running segment_hair with a dummy image...")
hair_mask_output = segment_hair(dummy_image_np) hair_mask_output = segment_hair(dummy_image_np)
@ -110,7 +95,7 @@ if __name__ == '__main__':
# Check if the mask is binary (0 or 255) # Check if the mask is binary (0 or 255)
assert np.all(np.isin(hair_mask_output, [0, 255])) assert np.all(np.isin(hair_mask_output, [0, 255]))
print("Dummy image test successful. Hair mask seems to be generated correctly.") print("Dummy image test successful. Hair mask seems to be generated correctly.")
# Attempt to save the dummy mask (optional, just for visual confirmation if needed) # Attempt to save the dummy mask (optional, just for visual confirmation if needed)
# cv2.imwrite("dummy_hair_mask_output.png", hair_mask_output) # cv2.imwrite("dummy_hair_mask_output.png", hair_mask_output)
# print("Dummy hair mask saved to dummy_hair_mask_output.png") # print("Dummy hair mask saved to dummy_hair_mask_output.png")

102
modules/processors/frame/face_swapper.py
View File

@ -69,70 +69,34 @@ def get_face_swapper() -> Any:
def _prepare_warped_source_material_and_mask( def _prepare_warped_source_material_and_mask(
source_face_obj: Face, source_face_obj: Face,
source_frame_full: Frame, source_frame_full: Frame,
matrix: np.ndarray, matrix: np.ndarray,
dsize: tuple # Built-in tuple is fine here for parameter type dsize: tuple # Built-in tuple is fine here for parameter type
) -> Tuple[Optional[Frame], Optional[Frame]]: ) -> Tuple[Optional[Frame], Optional[Frame]]:
""" """
Prepares warped source material (full image) and a combined (face+hair) mask for blending. Prepares warped source material (full image) and a combined (face+hair) mask for blending.
Returns (None, None) if essential masks cannot be generated. Returns (None, None) if essential masks cannot be generated.
""" """
try: # Generate Hair Mask
# Generate Hair Mask hair_only_mask_source_raw = segment_hair(source_frame_full)
hair_only_mask_source_raw = segment_hair(source_frame_full) if hair_only_mask_source_raw.ndim == 3 and hair_only_mask_source_raw.shape[2] == 3:
if hair_only_mask_source_raw is None: hair_only_mask_source_raw = cv2.cvtColor(hair_only_mask_source_raw, cv2.COLOR_BGR2GRAY)
logging.error("segment_hair returned None, which is unexpected.") _, hair_only_mask_source_binary = cv2.threshold(hair_only_mask_source_raw, 127, 255, cv2.THRESH_BINARY)
return None, None
if hair_only_mask_source_raw.ndim == 3 and hair_only_mask_source_raw.shape[2] == 3:
hair_only_mask_source_raw = cv2.cvtColor(hair_only_mask_source_raw, cv2.COLOR_BGR2GRAY)
_, hair_only_mask_source_binary = cv2.threshold(hair_only_mask_source_raw, 127, 255, cv2.THRESH_BINARY)
except Exception as e:
logging.error(f"Hair segmentation failed: {e}", exc_info=True)
return None, None
try: # Generate Face Mask
# Generate Face Mask face_only_mask_source_raw = create_face_mask(source_face_obj, source_frame_full)
face_only_mask_source_raw = create_face_mask(source_face_obj, source_frame_full) _, face_only_mask_source_binary = cv2.threshold(face_only_mask_source_raw, 127, 255, cv2.THRESH_BINARY)
if face_only_mask_source_raw is None:
logging.error("create_face_mask returned None, which is unexpected.")
return None, None
_, face_only_mask_source_binary = cv2.threshold(face_only_mask_source_raw, 127, 255, cv2.THRESH_BINARY)
except Exception as e:
logging.error(f"Face mask creation failed for source: {e}", exc_info=True)
return None, None
# Combine Face and Hair Masks and Warp # Combine Face and Hair Masks
try: if face_only_mask_source_binary.shape != hair_only_mask_source_binary.shape:
if face_only_mask_source_binary.shape != hair_only_mask_source_binary.shape:
logging.warning("Resizing hair mask to match face mask for source during preparation.")
hair_only_mask_source_binary = cv2.resize(
hair_only_mask_source_binary,
(face_only_mask_source_binary.shape[1], face_only_mask_source_binary.shape[0]),
interpolation=cv2.INTER_NEAREST
)
actual_combined_source_mask = cv2.bitwise_or(face_only_mask_source_binary, hair_only_mask_source_binary)
actual_combined_source_mask_blurred = cv2.GaussianBlur(actual_combined_source_mask, (5, 5), 3)
warped_full_source_material = cv2.warpAffine(source_frame_full, matrix, dsize)
warped_combined_mask_temp = cv2.warpAffine(actual_combined_source_mask_blurred, matrix, dsize)
_, warped_combined_mask_binary_for_clone = cv2.threshold(warped_combined_mask_temp, 127, 255, cv2.THRESH_BINARY)
except Exception as e:
logging.error(f"Mask combination or warping failed: {e}", exc_info=True)
return None, None
return warped_full_source_material, warped_combined_mask_binary_for_clone
def _blend_material_onto_frame(
logging.warning("Resizing hair mask to match face mask for source during preparation.") logging.warning("Resizing hair mask to match face mask for source during preparation.")
hair_only_mask_source_binary = cv2.resize( hair_only_mask_source_binary = cv2.resize(
hair_only_mask_source_binary, hair_only_mask_source_binary,
(face_only_mask_source_binary.shape[1], face_only_mask_source_binary.shape[0]), (face_only_mask_source_binary.shape[1], face_only_mask_source_binary.shape[0]),
interpolation=cv2.INTER_NEAREST interpolation=cv2.INTER_NEAREST
) )
actual_combined_source_mask = cv2.bitwise_or(face_only_mask_source_binary, hair_only_mask_source_binary) actual_combined_source_mask = cv2.bitwise_or(face_only_mask_source_binary, hair_only_mask_source_binary)
actual_combined_source_mask_blurred = cv2.GaussianBlur(actual_combined_source_mask, (5, 5), 3) actual_combined_source_mask_blurred = cv2.GaussianBlur(actual_combined_source_mask, (5, 5), 3)
@ -140,13 +104,13 @@ def _blend_material_onto_frame(
warped_full_source_material = cv2.warpAffine(source_frame_full, matrix, dsize) warped_full_source_material = cv2.warpAffine(source_frame_full, matrix, dsize)
warped_combined_mask_temp = cv2.warpAffine(actual_combined_source_mask_blurred, matrix, dsize) warped_combined_mask_temp = cv2.warpAffine(actual_combined_source_mask_blurred, matrix, dsize)
_, warped_combined_mask_binary_for_clone = cv2.threshold(warped_combined_mask_temp, 127, 255, cv2.THRESH_BINARY) _, warped_combined_mask_binary_for_clone = cv2.threshold(warped_combined_mask_temp, 127, 255, cv2.THRESH_BINARY)
return warped_full_source_material, warped_combined_mask_binary_for_clone return warped_full_source_material, warped_combined_mask_binary_for_clone
def _blend_material_onto_frame( def _blend_material_onto_frame(
base_frame: Frame, base_frame: Frame,
material_to_blend: Frame, material_to_blend: Frame,
mask_for_blending: Frame mask_for_blending: Frame
) -> Frame: ) -> Frame:
""" """
@ -158,7 +122,7 @@ def _blend_material_onto_frame(
if w > 0 and h > 0: if w > 0 and h > 0:
center = (x + w // 2, y + h // 2) center = (x + w // 2, y + h // 2)
if material_to_blend.shape == base_frame.shape and \ if material_to_blend.shape == base_frame.shape and \
material_to_blend.dtype == base_frame.dtype and \ material_to_blend.dtype == base_frame.dtype and \
mask_for_blending.dtype == np.uint8: mask_for_blending.dtype == np.uint8:
@ -170,7 +134,7 @@ def _blend_material_onto_frame(
output_frame = cv2.seamlessClone(material_to_blend, base_frame, mask_for_blending, center, cv2.NORMAL_CLONE) output_frame = cv2.seamlessClone(material_to_blend, base_frame, mask_for_blending, center, cv2.NORMAL_CLONE)
except cv2.error as e: except cv2.error as e:
logging.warning(f"cv2.seamlessClone failed: {e}. Falling back to simple blending.") logging.warning(f"cv2.seamlessClone failed: {e}. Falling back to simple blending.")
boolean_mask = mask_for_blending > 127 boolean_mask = mask_for_blending > 127
output_frame[boolean_mask] = material_to_blend[boolean_mask] output_frame[boolean_mask] = material_to_blend[boolean_mask]
else: else:
logging.warning("Mismatch in shape/type for seamlessClone. Falling back to simple blending.") logging.warning("Mismatch in shape/type for seamlessClone. Falling back to simple blending.")
@ -178,7 +142,7 @@ def _blend_material_onto_frame(
output_frame[boolean_mask] = material_to_blend[boolean_mask] output_frame[boolean_mask] = material_to_blend[boolean_mask]
else: else:
logging.info("Warped mask for blending is empty. Skipping blending.") logging.info("Warped mask for blending is empty. Skipping blending.")
return output_frame return output_frame
@ -189,7 +153,7 @@ def swap_face(source_face_obj: Face, target_face: Face, source_frame_full: Frame
swapped_frame = face_swapper.get(temp_frame, target_face, source_face_obj, paste_back=True) swapped_frame = face_swapper.get(temp_frame, target_face, source_face_obj, paste_back=True)
final_swapped_frame = swapped_frame # Initialize with the base swap. Copy is made only if needed. final_swapped_frame = swapped_frame # Initialize with the base swap. Copy is made only if needed.
if getattr(modules.globals, 'enable_hair_swapping', True): # Default to True if attribute is missing if modules.globals.enable_hair_swapping:
if not (source_face_obj.kps is not None and \ if not (source_face_obj.kps is not None and \
target_face.kps is not None and \ target_face.kps is not None and \
source_face_obj.kps.shape[0] >= 3 and \ source_face_obj.kps.shape[0] >= 3 and \
@ -208,27 +172,23 @@ def swap_face(source_face_obj: Face, target_face: Face, source_frame_full: Frame
logging.warning("Failed to estimate affine transformation matrix for hair. Skipping hair blending.") logging.warning("Failed to estimate affine transformation matrix for hair. Skipping hair blending.")
else: else:
dsize = (temp_frame.shape[1], temp_frame.shape[0]) # width, height dsize = (temp_frame.shape[1], temp_frame.shape[0]) # width, height
warped_material, warped_mask = _prepare_warped_source_material_and_mask( warped_material, warped_mask = _prepare_warped_source_material_and_mask(
source_face_obj, source_frame_full, matrix, dsize source_face_obj, source_frame_full, matrix, dsize
) )
if warped_material is not None and warped_mask is not None: if warped_material is not None and warped_mask is not None:
# Make a copy only now that we are sure we will modify it for hair. # Make a copy only now that we are sure we will modify it for hair.
final_swapped_frame = swapped_frame.copy() final_swapped_frame = swapped_frame.copy()
try: color_corrected_material = apply_color_transfer(warped_material, final_swapped_frame) # Use final_swapped_frame for color context
color_corrected_material = apply_color_transfer(warped_material, final_swapped_frame)
except Exception as e:
logging.warning(f"Color transfer failed: {e}. Proceeding with uncorrected material for hair blending.", exc_info=True)
color_corrected_material = warped_material # Use uncorrected material as fallback
final_swapped_frame = _blend_material_onto_frame( final_swapped_frame = _blend_material_onto_frame(
final_swapped_frame, final_swapped_frame,
color_corrected_material, color_corrected_material,
warped_mask warped_mask
) )
# Mouth Mask Logic (operates on final_swapped_frame) # Mouth Mask Logic (operates on final_swapped_frame)
if modules.globals.mouth_mask: if modules.globals.mouth_mask:
# If final_swapped_frame wasn't copied for hair, it needs to be copied now before mouth mask modification. # If final_swapped_frame wasn't copied for hair, it needs to be copied now before mouth mask modification.

14
modules/ui.py
View File

@ -900,7 +900,7 @@ def create_webcam_preview(camera_index: int):
PREVIEW.deiconify() PREVIEW.deiconify()
frame_processors = get_frame_processors_modules(modules.globals.frame_processors) frame_processors = get_frame_processors_modules(modules.globals.frame_processors)
# --- Source Image Loading and Validation (Moved before the loop) --- # --- Source Image Loading and Validation (Moved before the loop) ---
source_face_obj_for_cam = None source_face_obj_for_cam = None
source_frame_full_for_cam = None source_frame_full_for_cam = None
@ -925,7 +925,7 @@ def create_webcam_preview(camera_index: int):
ROOT.update() ROOT.update()
time.sleep(0.05) time.sleep(0.05)
return return
source_frame_full_for_cam = cv2.imread(modules.globals.source_path) source_frame_full_for_cam = cv2.imread(modules.globals.source_path)
if source_frame_full_for_cam is None: if source_frame_full_for_cam is None:
update_status(f"Error: Could not read source image at {modules.globals.source_path}") update_status(f"Error: Could not read source image at {modules.globals.source_path}")
@ -980,7 +980,7 @@ def create_webcam_preview(camera_index: int):
ROOT.update() ROOT.update()
time.sleep(0.05) time.sleep(0.05)
return return
if not modules.globals.source_target_map and not modules.globals.simple_map: if not modules.globals.source_target_map and not modules.globals.simple_map:
update_status("Warning: No face map defined for map_faces mode. Swapper may not work as expected.") update_status("Warning: No face map defined for map_faces mode. Swapper may not work as expected.")
# This is a warning, not a fatal error for the preview window itself. Processing will continue. # This is a warning, not a fatal error for the preview window itself. Processing will continue.
@ -1015,11 +1015,11 @@ def create_webcam_preview(camera_index: int):
if not modules.globals.map_faces: if not modules.globals.map_faces:
# Case 1: map_faces is False - source_face_obj_for_cam and source_frame_full_for_cam are pre-loaded # Case 1: map_faces is False - source_face_obj_for_cam and source_frame_full_for_cam are pre-loaded
if source_face_obj_for_cam is not None and source_frame_full_for_cam is not None: # Check if valid after pre-loading if source_face_obj_for_cam and source_frame_full_for_cam is not None: # Check if valid after pre-loading
for frame_processor in frame_processors: for frame_processor in frame_processors:
if frame_processor.NAME == "DLC.FACE-ENHANCER": if frame_processor.NAME == "DLC.FACE-ENHANCER":
if modules.globals.fp_ui["face_enhancer"]: if modules.globals.fp_ui["face_enhancer"]:
temp_frame = frame_processor.process_frame(None, temp_frame) temp_frame = frame_processor.process_frame(None, temp_frame)
else: else:
temp_frame = frame_processor.process_frame(source_face_obj_for_cam, source_frame_full_for_cam, temp_frame) temp_frame = frame_processor.process_frame(source_face_obj_for_cam, source_frame_full_for_cam, temp_frame)
# If source image was invalid (e.g. no face), source_face_obj_for_cam might be None. # If source image was invalid (e.g. no face), source_face_obj_for_cam might be None.
@ -1032,10 +1032,8 @@ def create_webcam_preview(camera_index: int):
for frame_processor in frame_processors: for frame_processor in frame_processors:
if frame_processor.NAME == "DLC.FACE-ENHANCER": if frame_processor.NAME == "DLC.FACE-ENHANCER":
if modules.globals.fp_ui["face_enhancer"]: if modules.globals.fp_ui["face_enhancer"]:
# Corrected: face_enhancer.process_frame_v2 is expected to take only temp_frame temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame)
temp_frame = frame_processor.process_frame_v2(temp_frame)
else: else:
# This is for other processors when map_faces is True
temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame) temp_frame = frame_processor.process_frame_v2(source_frame_full_for_cam_map_faces, temp_frame)
# If source_frame_full_for_cam_map_faces was invalid, error is persistent from pre-loop check. # If source_frame_full_for_cam_map_faces was invalid, error is persistent from pre-loop check.

5
setup_windows.bat
View File

@ -15,9 +15,8 @@ if errorlevel 1 (
:: Optional: Check Python version (e.g., >= 3.9 or >=3.10). :: Optional: Check Python version (e.g., >= 3.9 or >=3.10).
:: This is a bit more complex in pure batch. For now, rely on user having a modern Python 3. :: This is a bit more complex in pure batch. For now, rely on user having a modern Python 3.
:: The README will recommend 3.10. :: The README will recommend 3.10.
:: If we reach here, Python is found. echo Found Python:
echo Python was found. Attempting to display version: python --version
for /f "delims=" %%i in ('python --version 2^>^&1') do echo %%i
:: 2. Check for ffmpeg (informational) :: 2. Check for ffmpeg (informational)
echo Checking for ffmpeg... echo Checking for ffmpeg...