update

app.py

@@ -1,7 +1,79 @@
 import gradio as gr
+import cv2
+import math
+import numpy as np
+import os
+import numpy as np
+import mediapipe as mp
 
-def greet(name):
-    return "Hello " + name + "!!"
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+print("hello world")
+# Height and width that will be used by the model
+DESIRED_HEIGHT = 480
+DESIRED_WIDTH = 480
+
+# Performs resizing and showing the image
+def resize_and_show(image):
+  h, w = image.shape[:2]
+  if h < w:
+    img = cv2.resize(image, (DESIRED_WIDTH, math.floor(h/(w/DESIRED_WIDTH))))
+  else:
+    img = cv2.resize(image, (math.floor(w/(h/DESIRED_HEIGHT)), DESIRED_HEIGHT))
+  cv2.imshow('color', img)
+  cv2.waitKey(1000)
+  cv2.destroyAllWindows()
+
+def segmentate(image):
+    BG_COLOR = (192, 192, 192) # gray
+    MASK_COLOR = (255, 255, 255) # white
+
+    # Create the options that will be used for ImageSegmenter
+    base_options = python.BaseOptions(model_asset_path='./hair_segmenter.tflite')
+    options = vision.ImageSegmenterOptions(base_options=base_options,output_category_mask=True)
+
+    # Create the image segmenter
+    with vision.ImageSegmenter.create_from_options(options) as segmenter:
+
+        # Create the MediaPipe image file that will be segmented
+        # image = mp.Image.create_from_file(image_file_name)
+
+        # Retrieve the masks for the segmented image
+        segmentation_result = segmenter.segment(image)
+        category_mask = segmentation_result.category_mask
+
+        # Generate solid color images for showing the output segmentation mask.
+        image_data = image.numpy_view()
+        fg_image = np.zeros(image_data.shape, dtype=np.uint8)
+        fg_image[:] = MASK_COLOR
+        bg_image = np.zeros(image_data.shape, dtype=np.uint8)
+        bg_image[:] = BG_COLOR
+
+        condition = np.stack((category_mask.numpy_view(),) * 3, axis=-1) > 0.2
+        output_image = np.where(condition, fg_image, bg_image)
+
+        # print(f'Segmentation mask of {name}:')
+        # resize_and_show(output_image)
+        return output_image
+
+# GUI
+title = 'mediapipe hair segmentation'
+description = 'hair segmentation using mediapipe'
+examples = [[f'examples/{name}', 3] for name in sorted(os.listdir('examples'))]
+
+iface = gr.Interface(
+    fn=segmentate,
+    inputs=[
+        gr.Image(type='pil', label='Input Image')
+    ],
+    outputs=[
+        gr.Image(label='image segmentated')
+    ],
+    examples=examples,
+    allow_flagging='never',
+    cache_examples=False,
+    title=title,
+    description=description
+)
 iface.launch()

hair_segmenter.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2628cf3ce5f695f604cbea2841e00befcaa3624bf80caf3664bef2656d59bf84
+size 781618

requirements.txt

@@ -0,0 +1,19 @@
+gradio==3.41.0
+absl-py
+attrs
+cffi
+contourpy
+cycler
+flatbuffers
+fonttools
+kiwisolver
+matplotlib
+mediapipe
+numpy
+opencv-contrib-python
+opencv-python
+pillow
+protobuf
+pycparser
+pyparsing
+sounddevice