Added logic

app.py

@@ -1,118 +1,127 @@
 import cv2
-import mediapipe as mp
 import numpy as np
 import gradio as gr
+import os
+
+# Install mediapipe if not available
+try:
+    import mediapipe as mp
+except ImportError:
+    import pip
+
+    pip.main(['install', 'mediapipe'])
+    import mediapipe as mp
 
-example_list = [["examples/" + example] for example in os.listdir("examples")]
 
 def process_face_image(input_image):
     """
-    ������� �������� ����������, ��������� ��������� �������,
-    � ������� ��� ����������: � ����������� �� � ������������
+    Function processes the image, finds facial landmarks,
+    and returns two images: one with landmarks and one with measurements
     """
-    # ���������� ���������� � gradio � ������ numpy
+    # Convert image from gradio to numpy format
     if input_image is None:
         return None, None
-    
+
     # Face mesh
     mp_face_mesh = mp.solutions.face_mesh
     face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, min_detection_confidence=0.5)
-    
-    # �������� ������ ����������
+
+    # Get image dimensions
     image = input_image.copy()
     height, width, _ = image.shape
     rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    
-    # ��������� ��ﳿ ���������� ��� ��������� � ����
+
+    # Create copies for landmarks and measurements
     image_all_landmarks = image.copy()
     image_with_lines = image.copy()
-    
-    # ��������� ���������
+
+    # Find landmarks
     result = face_mesh.process(rgb_image)
-    
-    # ����������, �� �������� �������
+
+    # Check if face was detected
     if not result.multi_face_landmarks:
-        return image, image, "������� �� ��������"
-    
-    # ���������� �������� ���������
+        return image, image, "No face detected"
+
+    # Process the found landmarks
     for facial_landmarks in result.multi_face_landmarks:
-        # ������� �� ��������� ���������� �������
+        # Draw all landmarks as thin points
         for i in range(0, 468):
             pt1 = facial_landmarks.landmark[i]
             x = int(pt1.x * width)
             y = int(pt1.y * height)
             cv2.circle(image_all_landmarks, (x, y), 1, (100, 100, 0), -1)
-            
-            # ������ ������ ��������� ��� �������� �����
+
+            # Add landmark numbers for important points
             if i in [10, 152, 234, 454, 35, 265, 129, 358]:
-                cv2.putText(image_all_landmarks, str(i), (x+2, y+2), 
-                          cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), 1)
-        
-        # ������ ������� (����� 234 � 454)
+                cv2.putText(image_all_landmarks, str(i), (x + 2, y + 2),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), 1)
+
+        # Face width (points 234 and 454)
         right_face = facial_landmarks.landmark[234]
         left_face = facial_landmarks.landmark[454]
         right_x = int(right_face.x * width)
         right_y = int(right_face.y * height)
         left_x = int(left_face.x * width)
         left_y = int(left_face.y * height)
-        
-        # ������� ���� ������ �������
+
+        # Draw face width line
         cv2.line(image_with_lines, (right_x, right_y), (left_x, left_y), (0, 255, 0), 3)
         face_width = ((left_x - right_x) ** 2 + (left_y - right_y) ** 2) ** 0.5
-        cv2.putText(image_with_lines, f"Face width: {face_width:.2f}px", 
+        cv2.putText(image_with_lines, f"Face width: {face_width:.2f}px",
                     (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
-        
-        # ³������ �� ����� (����� 35 � 265)
+
+        # Eye distance (points 35 and 265)
         right_eye = facial_landmarks.landmark[35]
         left_eye = facial_landmarks.landmark[265]
         right_eye_x = int(right_eye.x * width)
         right_eye_y = int(right_eye.y * height)
         left_eye_x = int(left_eye.x * width)
         left_eye_y = int(left_eye.y * height)
-        
-        # ������� ���� ������� �� �����
+
+        # Draw eye distance line
         cv2.line(image_with_lines, (right_eye_x, right_eye_y), (left_eye_x, left_eye_y), (255, 0, 0), 3)
         eye_distance = ((left_eye_x - right_eye_x) ** 2 + (left_eye_y - right_eye_y) ** 2) ** 0.5
-        cv2.putText(image_with_lines, f"Eye distance: {eye_distance:.2f}px", 
+        cv2.putText(image_with_lines, f"Eye distance: {eye_distance:.2f}px",
                     (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
-        
-        # ������ ���� (����� 129 � 358)
+
+        # Nose width (points 129 and 358)
         right_nose = facial_landmarks.landmark[129]
         left_nose = facial_landmarks.landmark[358]
         right_nose_x = int(right_nose.x * width)
         right_nose_y = int(right_nose.y * height)
         left_nose_x = int(left_nose.x * width)
         left_nose_y = int(left_nose.y * height)
-        
-        # ������� ���� ������ ����
+
+        # Draw nose width line
         cv2.line(image_with_lines, (right_nose_x, right_nose_y), (left_nose_x, left_nose_y), (255, 165, 0), 3)
         nose_width = ((left_nose_x - right_nose_x) ** 2 + (left_nose_y - right_nose_y) ** 2) ** 0.5
-        cv2.putText(image_with_lines, f"Nose width: {nose_width:.2f}px", 
+        cv2.putText(image_with_lines, f"Nose width: {nose_width:.2f}px",
                     (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 165, 0), 2)
-        
-        # ������ ������� (����� 10 � 152)
-        forehead = facial_landmarks.landmark[10]  # ����� �� ���
-        chin = facial_landmarks.landmark[152]     # ����� �� �������
+
+        # Face height (points 10 and 152)
+        forehead = facial_landmarks.landmark[10]  # Forehead point
+        chin = facial_landmarks.landmark[152]  # Chin point
         forehead_x = int(forehead.x * width)
         forehead_y = int(forehead.y * height)
         chin_x = int(chin.x * width)
         chin_y = int(chin.y * height)
-        
-        # ������� ���� ������ �������
+
+        # Draw face height line
         cv2.line(image_with_lines, (forehead_x, forehead_y), (chin_x, chin_y), (0, 0, 255), 3)
         face_height = ((chin_x - forehead_x) ** 2 + (chin_y - forehead_y) ** 2) ** 0.5
-        cv2.putText(image_with_lines, f"Face height: {face_height:.2f}px", 
+        cv2.putText(image_with_lines, f"Face height: {face_height:.2f}px",
                     (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
-        
-        # ��������� ��������� �������
+
+        # Return face ratio
         face_ratio = face_width / face_height if face_height > 0 else 0
-        cv2.putText(image_with_lines, f"Face ratio: {face_ratio:.2f}", 
+        cv2.putText(image_with_lines, f"Face ratio: {face_ratio:.2f}",
                     (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 255), 2)
-    
-    # ��������� ������ ����������
+
+    # Return both images
     return image_all_landmarks, image_with_lines
 
-# ��������� ��������� Gradio
+
+# Create Gradio interface
 demo = gr.Interface(
     fn=process_face_image,
     inputs=[
@@ -122,17 +131,37 @@ demo = gr.Interface(
         gr.Image(type="numpy", label="Face Landmarks"),
         gr.Image(type="numpy", label="Face Measurements")
     ],
-    title="Sytoss: Face Analysis with Measurements",
+    title="Face Analysis with Measurements",
     description="""
     Upload a face image to get:
-    1. An image with all landmark points
-    2. An image with measurements (face width, eye distance, nose width, face height)
+    1. Image with all landmark points
+    2. Image with measurements (face width, eye distance, nose width, face height)
     """,
-    examples=[
-        examples=example_list
-    ]
 )
 
-# ��������� ���������
+# Add examples from the 'examples' directory if it exists
+if os.path.exists("examples"):
+    example_list = [["examples/" + example] for example in os.listdir("examples") if
+                    example.endswith(('.jpg', '.jpeg', '.png'))]
+    if example_list:
+        demo = gr.Interface(
+            fn=process_face_image,
+            inputs=[
+                gr.Image(type="numpy", label="Input Image")
+            ],
+            outputs=[
+                gr.Image(type="numpy", label="Face Landmarks"),
+                gr.Image(type="numpy", label="Face Measurements")
+            ],
+            title="Face Analysis with Measurements",
+            description="""
+            Upload a face image to get:
+            1. Image with all landmark points
+            2. Image with measurements (face width, eye distance, nose width, face height)
+            """,
+            examples=example_list
+        )
+
+# Launch the interface
 if __name__ == "__main__":
-    demo.launch(share=True)  # share=True �������� �������� ������� ���������
+    demo.launch(share=True)  # share=True allows you to get a public link

requirements.txt

@@ -2,4 +2,4 @@ gradio==3.49.0
 numpy==1.26.0
 opencv-python==4.9.0.80
 ultralytics==8.1.10
-mediapipe==0.10.7
+mediapipe==0.10.13