Add face detection

code/blur.py

+from PIL import Image, ImageFilter
+import numpy as np
+import cv2
+
+# Load the image from file
+file_path = '/Users/dakotagoldberg/cba/raspi_drawing_machine/code/input/test.jpg'
+image = cv2.imread(file_path)
+
+# Convert the image to RGB (OpenCV uses BGR by default)
+image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+# Use haarcascade_frontalface_default.xml to detect faces
+face_cascade = cv2.CascadeClassifier(
+    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Detect faces in the image
+faces = face_cascade.detectMultiScale(
+    image_rgb, scaleFactor=1.8, minNeighbors=5, minSize=(50, 50))
+
+# Create a mask where the faces are 0 and the rest is 1
+mask = np.ones_like(image) * 255  # White mask
+for (x, y, w, h) in faces:
+    mask[y:y+h, x:x+w] = 0  # Set the face region to black
+
+# Convert mask to boolean
+mask_bool = mask[:, :, 0] == 0
+
+image[~mask_bool] = 255  # Set the face region to white
+
+
+# # Create a blurred version of the original image
+# blurred_image = cv2.GaussianBlur(image, (25, 25), 0)
+
+# # Copy the clear face region from the original image
+# final_image = image.copy()
+# final_image[~mask_bool] = blurred_image[~mask_bool]
+
+# Convert the final image to PIL format for saving
+final_image_pil = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+# Save the final image
+output_path = '/Users/dakotagoldberg/cba/raspi_drawing_machine/code/output/result.jpg'
+final_image_pil.save(output_path)