first commit

app.py

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+import gradio as gr
+import cv2
+import math
+import numpy as np
+import os
+import mediapipe as mp
+
+import sys 
+sys.path.append('/home/julien/Documents/bald_classification')
+
+import predict
+
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+
+# 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(filepath):
+    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:
+
+      # Loop through demo image(s)
+
+        # Create the MediaPipe image file that will be segmented
+        print(filepath)
+        image = mp.Image.create_from_file(filepath)
+        
+        # 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)
+        prediction = predict.predict(filepath)[0][0]
+        print(prediction)
+        limits = [0.002, 0.1, 0.4, 0.95, 0.97, 0.991, 1]
+        print(np.where(prediction < limits)[0][0])
+        """
+        img = cv2.cvtColor(output_image, cv2.COLOR_BGR2GRAY)
+        # print(np.unique(img))
+        _, thresh = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY_INV)
+        # plt.imshow(thresh, cmap='gray')
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        im = cv2.drawContours(img, contours, -1, (0,255,0), 3)
+        """
+        return np.where(prediction < limits)[0][0] + 1, output_image
+
+# GUI
+title = 'Hair loss prediction'
+description = 'Automatic Prediction of Nordwood scale state'
+examples = [[f'examples/{name}', 3] for name in sorted(os.listdir('examples'))]
+
+iface = gr.Interface(
+    fn=segmentate,
+    inputs=[
+        gr.Image(type='filepath', label='Input Image')
+    ],
+    outputs=[
+        gr.Number(label='Nordwood Scale'), gr.Image(label='Hair Segmentation')
+    ],
+    examples=examples,
+    allow_flagging='never',
+    cache_examples=False,
+    title=title,
+    description=description
+)
+iface.launch()

hair_segmenter.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2628cf3ce5f695f604cbea2841e00befcaa3624bf80caf3664bef2656d59bf84
+size 781618

requirements.txt

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+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