New filters: Noise, grayscale and oily skin detection

DPF/filters/data_filter.py

@@ -5,7 +5,7 @@
 
 import pandas as pd
 from torch.utils.data import DataLoader, Dataset
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from DPF.dataloaders.dataloader_utils import identical_collate_fn
 from DPF.modalities import ModalityName

DPF/filters/images/face_focus_filter.py

@@ -0,0 +1,183 @@
+import os
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List
+import cv2
+import torch
+from torch.multiprocessing import Pool, set_start_method
+# Set the start method to 'spawn' at the beginning of your script
+try:
+    set_start_method('spawn')
+except RuntimeError:
+    pass
+from functools import partial
+import numpy as np
+from scipy.stats import kurtosis
+from DPF.types import ModalityToDataMapping
+from DPF.utils import read_image_rgb_from_bytes
+from .img_filter import ImageFilter
+from PIL import Image, UnidentifiedImageError
+from retinaface.pre_trained_models import get_model
+
+class FaceFocusFilter(ImageFilter):
+    def __init__(
+        self,
+        threshold: float = 2000.0,
+        detect_face = True,
+        workers: int = 1,
+        batch_size: int = 1,
+        pbar: bool = True,
+        device=None,
+        _pbar_position: int = 0
+    ):
+        super().__init__(pbar, _pbar_position)
+        self.threshold = threshold
+        self.detect_face = detect_face
+        self.num_workers = workers
+        self.batch_size = batch_size
+        if not device:
+            self.device = 'cuda' if torch.cuda.is_available() else "cpu"
+        else:
+            self.device = device
+        self.face_detector = get_model("resnet50_2020-07-20", 
+                                       max_size=2048,
+                                       device=self.device)
+        self.face_detector.eval()
+
+
+    @property
+    def result_columns(self) -> list[str]:
+        return ["face_focus_measure", "bg_focus_measure", "bbox", "faces_count", "confidence", "face_pass"]
+
+    @property
+    def dataloader_kwargs(self) -> dict[str, Any]:
+        return {
+            "num_workers": self.num_workers,
+            "batch_size": self.batch_size,
+            "drop_last": False,
+        }
+
+    def preprocess_data(
+        self,
+        modality2data: ModalityToDataMapping,
+        metadata: dict[str, Any]
+    ) -> Any:
+        key = metadata[self.key_column]
+        try:
+            pil_image = read_image_rgb_from_bytes(modality2data['image'])
+            numpy_image = np.array(pil_image)
+            opencv_image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR)
+            return key, opencv_image
+        except (OSError, UnidentifiedImageError, ValueError) as e:
+            print(f"Error processing image for key {key}: {str(e)}")
+            return key, None
+
+    def process_batch(self, batch: list[Any]) -> dict[str, list[Any]]:
+        df_batch_labels = self._get_dict_from_schema()
+
+        for key, image in batch:
+            info = self.process_image(image)
+
+            if info:
+                df_batch_labels["face_focus_measure"].append(info["face_focus_measure"])
+                df_batch_labels["bg_focus_measure"].append(info["bg_focus_measure"])
+                df_batch_labels["bbox"].append(info["bbox"])
+                df_batch_labels["faces_count"].append(info["faces_count"])
+                df_batch_labels["confidence"].append(info["confidence"])
+                df_batch_labels["face_pass"].append(info["face_pass"])
+            else:
+                df_batch_labels["face_focus_measure"].append(0)
+                df_batch_labels["bg_focus_measure"].append(0)
+                df_batch_labels["bbox"].append(False)
+                df_batch_labels["faces_count"].append(0)
+                df_batch_labels["confidence"].append(0.0)
+                df_batch_labels["face_pass"].append(False)
+
+            df_batch_labels[self.key_column].append(key)
+
+        return df_batch_labels
+
+    # def process_batch(self, batch: list[Any]) -> dict[str, list[Any]]:
+    #     df_batch_labels = self._get_dict_from_schema()
+
+    #     # Create a partial function with self.process_image
+    #     process_image_partial = partial(self.process_image)
+
+    #     # Use multiprocessing to process images in parallel
+    #     with Pool() as pool:
+    #         results = pool.map(process_image_partial, [image for _, image in batch])
+
+    #     for (key, _), info in zip(batch, results):
+    #         for column in self.result_columns:
+    #             df_batch_labels[column].append(info.get(column, 0 if column in ['face_focus_measure', 'bg_focus_measure', 'faces_count', 'confidence'] else False))
+    #         df_batch_labels[self.key_column].append(key)
+
+    #     return df_batch_labels
+
+    def tenengrad_variance(self, image):
+        """
+        Calculate the Tenengrad variance focus measure for the given image.
+        """
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        gx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
+        gy = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
+        gx_squared = np.square(gx)
+        gy_squared = np.square(gy)
+        tenengrad_variance = np.mean(gx_squared + gy_squared)
+        return tenengrad_variance
+
+    def process_image(self, image):
+
+        # Calculate the focus measure for the entire image
+        bg_focus_measure = self.tenengrad_variance(image)
+
+        if not self.detect_face:
+            # Check if the face is in focus
+            in_focus = bg_focus_measure > self.threshold
+            return {
+                "face_focus_measure": 0,
+                "bg_focus_measure": bg_focus_measure,
+                "bbox": None,
+                "faces_count": 0,
+                "confidence":0,
+                "face_pass": in_focus
+            }
+
+        # Detect faces in the image
+        faces = self.face_detector.predict_jsons(image)
+
+        if faces is None or len(faces) == 0:
+            return {
+                "face_focus_measure": 0,
+                "bg_focus_measure": bg_focus_measure,
+                "bbox": None,
+                "faces_count": 0,
+                "confidence": 0,
+                "face_pass": False
+            }
+
+        # Get the face with the highest confidence
+        face = max(faces, key=lambda x: x['score'])
+
+        faces = [x for x in faces if x['score'] > 0.5]
+
+        bbox = face['bbox']
+        landmarks = face['landmarks']
+
+        # Extract the face region
+        x1, y1, x2, y2 = map(int, bbox)
+        face_region = image[y1:y2, x1:x2]
+
+        # Calculate the focus measure for the face region
+        face_focus_measure = self.tenengrad_variance(face_region)
+
+        # Check if the face is in focus
+        in_focus = face_focus_measure > self.threshold
+
+        return {
+            "face_focus_measure": face_focus_measure,
+            "bg_focus_measure": bg_focus_measure,
+            "bbox": bbox,
+            "faces_count": len(faces),
+            "confidence": face["score"],
+            "face_pass": (len(faces) == 1) and in_focus and face['score'] > 0.5
+        }

DPF/filters/images/focus_peaking_filter.py

@@ -0,0 +1,133 @@
+import os
+from typing import Any
+from deepface import DeepFace
+import cv2
+import numpy as np
+from scipy.stats import kurtosis
+from DPF.types import ModalityToDataMapping
+from DPF.utils import read_image_rgb_from_bytes
+from .img_filter import ImageFilter
+
+class FocusFilter(ImageFilter):
+    """
+    Filter for detecting faces and checking if the face is in focus.
+
+    Parameters
+    ----------
+    face_focus_threshold: float = 2000.0
+        Threshold value for the Tenengrad variance focus measure to determine if the face is in focus.
+    workers: int = 16
+        Number of processes to use for reading data and calculating focus scores.
+    batch_size: int = 64
+        Batch size for processing images.
+    pbar: bool = True
+        Whether to use a progress bar.
+    """
+
+    def __init__(
+        self,
+        threshold: float = 2000.0,
+        workers: int = 1,
+        batch_size: int = 1,
+        pbar: bool = True,
+        _pbar_position: int = 0,
+        detect_face = True
+    ):
+        super().__init__(pbar, _pbar_position)
+        self.threshold = threshold
+        self.num_workers = workers
+        self.batch_size = batch_size
+        self.detect_face = detect_face
+
+    @property
+    def result_columns(self) -> list[str]:
+        return ["in_focus", "focus_measure"]
+
+    @property
+    def dataloader_kwargs(self) -> dict[str, Any]:
+        return {
+            "num_workers": self.num_workers,
+            "batch_size": self.batch_size,
+            "drop_last": False,
+        }
+
+    def preprocess_data(
+        self,
+        modality2data: ModalityToDataMapping,
+        metadata: dict[str, Any]
+    ) -> Any:
+        key = metadata[self.key_column]
+        pil_image = read_image_rgb_from_bytes(modality2data['image'])
+        image = np.array(pil_image)
+        return key, image
+
+    def process_batch(self, batch: list[Any]) -> dict[str, list[Any]]:
+        df_batch_labels = self._get_dict_from_schema()
+
+        for key, image in batch:
+            face_info = process_image(image, threshold=self.threshold)
+            if face_info:
+                df_batch_labels["face_detected"].append(True)
+                df_batch_labels["face_in_focus"].append(face_info["face_in_focus"])
+                df_batch_labels["face_focus_measure"].append(face_info["face_focus_measure"])
+            else:
+                df_batch_labels["face_detected"].append(False)
+                df_batch_labels["face_in_focus"].append(False)
+                df_batch_labels["face_focus_measure"].append(0.0)
+            df_batch_labels[self.key_column].append(key)
+
+        return df_batch_labels
+
+def tenengrad_variance(image):
+    """
+    Calculate the Tenengrad variance focus measure for the given image.
+    """
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
+    gy = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
+    gx_squared = np.square(gx)
+    gy_squared = np.square(gy)
+    tenengrad_variance = np.mean(gx_squared + gy_squared)
+    return tenengrad_variance
+
+def process_image(image, threshold=2000.0):
+    # Calculate the focus measure for the entire image
+    focus_measure = tenengrad_variance(image)
+
+    if not detect_faces:
+        return focus_measure
+
+    # Detect faces in the image
+    faces = DeepFace.extract_faces(image, 
+                    enforce_detection=False, 
+                    detector_backend='retinaface')
+
+    # Filter faces based on confidence and presence of both eyes
+    filtered_faces = [face for face in faces if face['confidence'] > 0.1]
+    if not filtered_faces:
+        return None
+
+    face = max(filtered_faces, key=lambda x: x['confidence'])
+
+    # Check if exactly one face is detected after filtering
+    if len(filtered_faces) == 1 and face['confidence'] > 0.5:
+        face['facial_area']['confidence'] = face['confidence']
+        if face['facial_area']['left_eye'] is not None and face['facial_area']['right_eye'] is not None:
+
+
+
+            # Extract the face region
+            x, y, w, h = face['facial_area']['x'], face['facial_area']['y'], face['facial_area']['w'], face['facial_area']['h']
+            face_region = image[y:y+h, x:x+w]
+
+            # Calculate the focus measure for the face region
+            face_focus_measure = tenengrad_variance(face_region)
+
+            # Check if the face is in focus
+            face_in_focus = face_focus_measure > threshold
+
+            # Add the focus information to the face dictionary
+            face['facial_area']['face_in_focus'] = face_in_focus
+            face['facial_area']['face_focus_measure'] = face_focus_measure
+
+            return face['facial_area']