added in the eye segmentation code

app.py

@@ -1,6 +1,8 @@
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
+import cv2
+import numpy as np
 
 # Function to classify the face shape
 def classify_face_shape(image):
@@ -71,7 +73,8 @@ def classify_eye_shape(image):
     pipe = pipeline("image-classification", model="justingrammens/eye-shape")
     
     # Run the pipeline on the uploaded image
-    output = pipe(image)
+    #output = pipe(image)
+    output = pipe("eye_regions.jpg") # use the eye_regions image instead
     
     print("Pipeline output for eye shape:", output)
     # Format the output to be compatible with gr.outputs.Label
@@ -84,7 +87,8 @@ def classify_eye_color(image):
     pipe = pipeline("image-classification", model="justingrammens/eye-color")
     
     # Run the pipeline on the uploaded image
-    output = pipe(image)
+    #output = pipe(image)
+    output = pipe("eye_regions.jpg") #use the eye_regions image instead
     
     print("Pipeline output for eye color:", output)
     # Format the output to be compatible with gr.outputs.Label
@@ -92,8 +96,15 @@ def classify_eye_color(image):
     
     return formatted_output
     
+
+def process_gradio_image(pil_image):
+    # Convert PIL image to NumPy array
+    image = np.array(pil_image)
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert RGB (from PIL) to BGR (OpenCV default)
+    return image
     
 def classify_image_with_multiple_models(image):
+    create_eye_region(image)
     face_shape_result = classify_face_shape(image)
     age_result = classify_age(image)
     skin_type_result = classify_skin_type(image)
@@ -105,6 +116,54 @@ def classify_image_with_multiple_models(image):
     return face_shape_result, age_result, skin_type_result, acne_results, hair_color_results, eye_shape, eye_color
 
 
+def create_eye_region(image):
+    # Load the pre-trained face detector
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+
+    image = process_gradio_image(image)
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+
+    for (x, y, w, h) in faces:
+        # Draw a rectangle around the face
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+
+        # Region of Interest (ROI) for the face
+        roi_gray = gray[y:y + h, x:x + w]
+        roi_color = image[y:y + h, x:x + w]
+
+        # Detect eyes in the face ROI
+        eyes = eye_cascade.detectMultiScale(roi_gray)
+
+        for (ex, ey, ew, eh) in eyes:
+            # Draw a rectangle around the eyes
+            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
+
+            # Extract the eye region
+            eye_roi = roi_color[ey:ey + eh, ex:ex + ew]
+            cv2.imwrite('eye_regions.jpg', eye_roi)
+
+            # Calculate the average color of the eye region
+            avg_color = np.mean(eye_roi, axis=(0, 1))
+
+            # Classify eye color based on average color
+            if avg_color[0] > avg_color[1] and avg_color[0] > avg_color[2]:
+                color = "Brown"
+            elif avg_color[1] > avg_color[0] and avg_color[1] > avg_color[2]:
+                color = "Green"
+            else:
+                color = "Blue"
+
+            # Display the eye color
+            cv2.putText(image, color, (ex, ey - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+
+            cv2.imwrite('segmented_face.jpg', image)
+
+
 # Create the Gradio interface
 demo = gr.Interface(
     fn=classify_image_with_multiple_models,  # The function to run