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app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import cv2
+import mediapipe as mp
+import urllib.request
+import numpy as np
+import pickle
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib import animation
+
+
+# In[2]:
+
+
+from PIL import Image
+from IPython.display import Video
+
+
+# In[3]:
+
+
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_holistic = mp.solutions.holistic
+mp_pose = mp.solutions.pose
+mp_face_mesh = mp.solutions.face_mesh
+
+
+# In[4]:
+
+
+face_url = "http://claireye.com.tw/img/20230222.jpg"
+urllib.request.urlretrieve(face_url, "face_image.jpg")
+
+
+# In[5]:
+
+
+# Fetch image for analysis
+img_url = "http://claireye.com.tw/img/230212a.jpg"
+urllib.request.urlretrieve(img_url, "pose.jpg")
+
+
+# In[6]:
+
+
+import gradio as gr
+
+
+# In[7]:
+
+
+mp_selfie = mp.solutions.selfie_segmentation
+
+
+# In[8]:
+
+
+def segment(image): 
+    with mp_selfie.SelfieSegmentation(model_selection=0) as model: 
+        res = model.process(image)
+        mask = np.stack((res.segmentation_mask,)*3, axis=-1) > 0.5 
+        return np.where(mask, image, cv2.blur(image, (40,40)))
+
+
+# In[9]:
+
+
+def facego(image): 
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True,
+        max_num_faces=1,
+        refine_landmarks=True,
+        min_detection_confidence=0.5) as face_mesh:
+
+    # Read image file with cv2 and convert from BGR to RGB
+        results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+  
+        annotated_image = image.copy()
+        for face_landmarks in results.multi_face_landmarks:
+        
+            mp_drawing.draw_landmarks(
+            image=annotated_image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_CONTOURS,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+                .get_default_face_mesh_contours_style())
+                
+            mp_drawing.draw_landmarks(
+            image=annotated_image,
+            landmark_list=face_landmarks,
+            connections=mp_face_mesh.FACEMESH_IRISES,
+            landmark_drawing_spec=None,
+            connection_drawing_spec=mp_drawing_styles
+                .get_default_face_mesh_iris_connections_style())
+
+        return annotated_image    
+
+
+# In[10]:
+
+
+def posego(image): 
+# Create a MediaPipe `Pose` object
+    with mp_pose.Pose(static_image_mode=True,
+                  model_complexity=2,
+                  enable_segmentation=True) as pose:
+        
+        results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+# Copy the iamge
+        annotated_image = image.copy()
+
+# Draw pose, left and right hands, and face landmarks on the image with drawing specification defaults.
+        mp_drawing.draw_landmarks(annotated_image, 
+                          results.pose_landmarks, 
+                          mp_pose.POSE_CONNECTIONS,
+                          landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+
+
+        return annotated_image  
+
+
+# In[11]:
+
+
+def inference(img, version):
+    print(version)
+    print("1")    
+    print(img)    
+    img2 = cv2.imread(img)
+    print("2")      
+    print(img2)    
+    if version == 'face':
+            img1=facego(img2)
+            print("1a")            
+    elif (version == 'pose'):
+            img1=posego(img2)
+            print("2a")            
+    else:
+            img1=segment(img2) 
+            print("3a")            
+    print("3")      
+    print(img1)
+    save_path = f'out.jpg'
+    cv2.imwrite(save_path, img1) 
+    img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
+    return img1, save_path
+
+
+# In[12]:
+
+
+title = "pose-style"
+description = "Gradio demo for pose-style. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='http://claireye.com.tw'>Claireye</a> | 2023</p>"
+
+
+# In[13]:
+
+
+gr.Interface(
+    inference, [
+        gr.inputs.Image(type="filepath",label="Input"),
+        gr.inputs.Radio(['face', 'pose', 'seg'], type="value", default='pose', label='mode')
+    ], [
+        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
+        gr.outputs.File(label="Download the output image")
+    ],
+    title=title,
+    description=description,
+    article=article,
+    examples=[['face_image.jpg', 'face'], ['pose.jpg', 'pose'], 
+              ['pose.jpg', 'seg']]).launch()
+
+
+# In[ ]:
+
+
+
+

requirements.txt

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+numpy
+opencv-python-headless
+mediapipe
+