Revert "face detection algo"

This reverts commit 9ac134d.

dataset-cleaning.py

@@ -1,11 +1,8 @@
 import os
 import shutil
 from PIL import Image
-import cv2
 from torchvision import transforms
 
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-
 # Define the simplified transform pipeline
 transform_pipeline = transforms.Compose([
     transforms.Resize((256, 256)),
@@ -54,35 +51,11 @@ def process_expression(expression_path, processed_path):
             process_image(img_path, processed_path, img_name)
 
 
-def detect_and_crop_face(img_cv):
-    gray = cv2.cvtColor(img_cv, cv2.COLOR_BGR2GRAY)
-    # Detect faces in the image
-    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),
-                                          flags=cv2.CASCADE_SCALE_IMAGE)
-    # If no faces are detected, return None
-    if len(faces) == 0:
-        return None
-    faces = sorted(faces, key=lambda x: x[2] * x[3], reverse=True)  # Sort by area (w * h)
-    x, y, w, h = faces[0]  # Choose the largest face
-    # Crop the face from the image
-    face_crop = img_cv[y:y + h, x:x + w]
-    return face_crop
-
-
 def process_image(img_path, processed_path, img_name):
-    img_cv = cv2.imread(img_path)
-    # Use the detect_and_crop_face function to get the cropped face image
-    face_crop_cv = detect_and_crop_face(img_cv)
-    # If a face was detected and cropped
-    if face_crop_cv is not None:
-        # Convert the cropped face to PIL Image
-        face_crop_pil = Image.fromarray(cv2.cvtColor(face_crop_cv, cv2.COLOR_BGR2RGB))
-
-        img_transformed = transform_pipeline(face_crop_pil)
-        img_processed = transforms.ToPILImage()(img_transformed)
-        img_processed.save(os.path.join(processed_path, img_name))
-    else:
-        print(f"No face detected in {img_name}, skipped.")
+    img = Image.open(img_path).convert('RGB')
+    img_transformed = transform_pipeline(img)
+    img_processed = transforms.ToPILImage()(img_transformed)
+    img_processed.save(os.path.join(processed_path, img_name))
 
 
 expressions = ['Happy', 'Neutral', 'Suprised', 'Focused']

requirements.txt

@@ -28,6 +28,3 @@ torch==2.2.0
 torchvision==0.17.0
 typing_extensions==4.9.0
 urllib3==2.2.1
-
-opencv-python~=4.9.0.80
-tqdm~=4.66.2