added interfaces
Browse files- app.py +135 -4
- examples/246416_1_mrvwallpaper_246416_1_org_eder.jpg +0 -0
- examples/Dzholi.jpg +0 -0
- examples/Kathrin-Eder.jpg +0 -0
- examples/OIP (1).jfif +0 -0
- examples/OIP (3).jfif +0 -0
- examples/OIP (6).jfif +0 -0
- examples/R.jfif +0 -0
- examples/_R___eder_katrin____par3f47be87fe7ac72343cb859f813231c2_dat1592459707.jpeg +0 -0
- examples/csm_EderT_BER_3333_070319_640_970b31d49d.jpg +0 -0
- examples/katharina-eder.1024x1024.jpg +0 -0
- examples/katrin-eder-c-landeshauptstadt-mainz_182-3276025_20210510064436.jpg +0 -0
- examples/thomas-eder.1024x1024 (1).jpg +0 -0
- requirements.txt +5 -0
app.py
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import gradio as gr
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return "Hello " + name + "!!"
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import cv2
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import mediapipe as mp
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import numpy as np
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import gradio as gr
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example_list = [["examples/" + example] for example in os.listdir("examples")]
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def process_face_image(input_image):
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"""
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������� �������� ����������, ��������� ��������� �������,
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� ������� ��� ����������: � ����������� �� � ������������
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"""
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# ���������� ���������� � gradio � ������ numpy
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if input_image is None:
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return None, None
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# Face mesh
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mp_face_mesh = mp.solutions.face_mesh
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face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, min_detection_confidence=0.5)
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# �������� ������ ����������
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image = input_image.copy()
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height, width, _ = image.shape
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rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# ��������� ��ﳿ ���������� ��� ��������� � ����
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image_all_landmarks = image.copy()
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image_with_lines = image.copy()
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# ��������� ���������
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result = face_mesh.process(rgb_image)
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# ����������, �� �������� �������
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if not result.multi_face_landmarks:
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return image, image, "������� �� ��������"
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# ���������� �������� ���������
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for facial_landmarks in result.multi_face_landmarks:
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# ������� �� ��������� ���������� �������
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for i in range(0, 468):
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pt1 = facial_landmarks.landmark[i]
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x = int(pt1.x * width)
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y = int(pt1.y * height)
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cv2.circle(image_all_landmarks, (x, y), 1, (100, 100, 0), -1)
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# ������ ������ ��������� ��� �������� �����
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if i in [10, 152, 234, 454, 35, 265, 129, 358]:
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cv2.putText(image_all_landmarks, str(i), (x+2, y+2),
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cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), 1)
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# ������ ������� (����� 234 � 454)
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right_face = facial_landmarks.landmark[234]
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left_face = facial_landmarks.landmark[454]
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right_x = int(right_face.x * width)
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right_y = int(right_face.y * height)
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left_x = int(left_face.x * width)
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left_y = int(left_face.y * height)
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# ������� ���� ������ �������
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cv2.line(image_with_lines, (right_x, right_y), (left_x, left_y), (0, 255, 0), 3)
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face_width = ((left_x - right_x) ** 2 + (left_y - right_y) ** 2) ** 0.5
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cv2.putText(image_with_lines, f"Face width: {face_width:.2f}px",
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(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
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# ³������ �� ����� (����� 35 � 265)
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right_eye = facial_landmarks.landmark[35]
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left_eye = facial_landmarks.landmark[265]
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right_eye_x = int(right_eye.x * width)
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right_eye_y = int(right_eye.y * height)
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left_eye_x = int(left_eye.x * width)
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left_eye_y = int(left_eye.y * height)
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# ������� ���� ������� �� �����
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cv2.line(image_with_lines, (right_eye_x, right_eye_y), (left_eye_x, left_eye_y), (255, 0, 0), 3)
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eye_distance = ((left_eye_x - right_eye_x) ** 2 + (left_eye_y - right_eye_y) ** 2) ** 0.5
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cv2.putText(image_with_lines, f"Eye distance: {eye_distance:.2f}px",
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(10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)
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# ������ ���� (����� 129 � 358)
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right_nose = facial_landmarks.landmark[129]
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left_nose = facial_landmarks.landmark[358]
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right_nose_x = int(right_nose.x * width)
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right_nose_y = int(right_nose.y * height)
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left_nose_x = int(left_nose.x * width)
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left_nose_y = int(left_nose.y * height)
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# ������� ���� ������ ����
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cv2.line(image_with_lines, (right_nose_x, right_nose_y), (left_nose_x, left_nose_y), (255, 165, 0), 3)
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nose_width = ((left_nose_x - right_nose_x) ** 2 + (left_nose_y - right_nose_y) ** 2) ** 0.5
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cv2.putText(image_with_lines, f"Nose width: {nose_width:.2f}px",
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(10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 165, 0), 2)
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# ������ ������� (����� 10 � 152)
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forehead = facial_landmarks.landmark[10] # ����� �� ���
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chin = facial_landmarks.landmark[152] # ����� �� �������
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forehead_x = int(forehead.x * width)
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forehead_y = int(forehead.y * height)
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chin_x = int(chin.x * width)
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chin_y = int(chin.y * height)
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# ������� ���� ������ �������
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cv2.line(image_with_lines, (forehead_x, forehead_y), (chin_x, chin_y), (0, 0, 255), 3)
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face_height = ((chin_x - forehead_x) ** 2 + (chin_y - forehead_y) ** 2) ** 0.5
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cv2.putText(image_with_lines, f"Face height: {face_height:.2f}px",
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(10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
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# ��������� ��������� �������
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face_ratio = face_width / face_height if face_height > 0 else 0
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cv2.putText(image_with_lines, f"Face ratio: {face_ratio:.2f}",
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(10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 255), 2)
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# ��������� ������ ����������
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return image_all_landmarks, image_with_lines
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# ��������� ��������� Gradio
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demo = gr.Interface(
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fn=process_face_image,
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inputs=[
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gr.Image(type="numpy", label="Input Image")
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],
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outputs=[
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gr.Image(type="numpy", label="Face Landmarks"),
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gr.Image(type="numpy", label="Face Measurements")
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],
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title="Sytoss: Face Analysis with Measurements",
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description="""
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Upload a face image to get:
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1. An image with all landmark points
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2. An image with measurements (face width, eye distance, nose width, face height)
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""",
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examples=[
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examples=example_list
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]
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)
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# ��������� ���������
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if __name__ == "__main__":
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demo.launch(share=True) # share=True �������� �������� ������� ���������
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examples/246416_1_mrvwallpaper_246416_1_org_eder.jpg
ADDED
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examples/Dzholi.jpg
ADDED
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examples/Kathrin-Eder.jpg
ADDED
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examples/OIP (1).jfif
ADDED
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Binary file (19.8 kB). View file
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examples/OIP (3).jfif
ADDED
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Binary file (28.8 kB). View file
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examples/OIP (6).jfif
ADDED
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Binary file (23.6 kB). View file
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examples/R.jfif
ADDED
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Binary file (127 kB). View file
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examples/_R___eder_katrin____par3f47be87fe7ac72343cb859f813231c2_dat1592459707.jpeg
ADDED
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examples/csm_EderT_BER_3333_070319_640_970b31d49d.jpg
ADDED
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examples/katharina-eder.1024x1024.jpg
ADDED
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examples/katrin-eder-c-landeshauptstadt-mainz_182-3276025_20210510064436.jpg
ADDED
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examples/thomas-eder.1024x1024 (1).jpg
ADDED
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requirements.txt
ADDED
|
@@ -0,0 +1,5 @@
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gradio==3.49.0
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numpy==1.26.0
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opencv-python==4.9.0.80
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ultralytics==8.1.10
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mediapipe==0.10.7
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