Upload 2 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+shape_predictor_68_face_landmarks.dat filter=lfs diff=lfs merge=lfs -text

main.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import dlib
+import cv2
+import numpy as np
+
+# Load the pre-trained face detector and facial landmarks predictor
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")  # You'll need to download this file
+
+# Function to detect eyes in the image
+def detect_eyes(image):
+    image = cv2.imdecode(np.frombuffer(image.read(), np.uint8), -1)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = detector(gray)
+
+    # Loop through detected faces
+    for face in faces:
+        landmarks = predictor(gray, face)
+
+        # Define regions of interest for left and right eyes
+        left_eye_region = [(36, 37, 38, 39, 40, 41)]
+        right_eye_region = [(42, 43, 44, 45, 46, 47)]
+
+        # Draw rectangles around eyes
+        for region in left_eye_region + right_eye_region:
+            for i in region:
+                x = landmarks.part(i).x
+                y = landmarks.part(i).y
+                cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
+
+    # Encode the image back to bytes
+    _, buffer = cv2.imencode('.jpg', image)
+    return buffer.tobytes()
+
+iface = gr.Interface(fn=detect_eyes, inputs=gr.inputs.Imagebox(), outputs=gr.outputs.Imagebox())
+iface.launch()

shape_predictor_68_face_landmarks.dat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbdc2cb80eb9aa7a758672cbfdda32ba6300efe9b6e6c7a299ff7e736b11b92f
+size 99693937