dev(narugo): remove usage of cv2

app.py

@@ -139,7 +139,7 @@ if __name__ == '__main__':
                                                 label='Zooming')
                 with gr.Row():
                     sd_f_breast = gr.Checkbox(value=False, label='Female\'s Breast')
-                    sd_f_breast_zoom = gr.Slider(value=0.6, minimum=0.1, maximum=3.0, step=0.05,
+                    sd_f_breast_zoom = gr.Slider(value=0.9, minimum=0.1, maximum=3.0, step=0.05,
                                                  label='Zooming')
                 with gr.Row():
                     sd_m_penis = gr.Checkbox(value=True, label='Male\'s Penis')

nudenet.py

@@ -2,14 +2,13 @@ from functools import lru_cache
 from pathlib import Path
 from typing import Tuple, List
 
-import cv2
 import numpy as np
 import onnxruntime
 from PIL import Image
 from huggingface_hub import hf_hub_download
 
 
-def read_image_bgr(image) -> np.ndarray:
+def _read_bgr_data(image) -> np.ndarray:
     """ Read an image in BGR format.
     Args
         path: Path to the image.
@@ -25,31 +24,27 @@ def _preprocess_image(x) -> np.ndarray:
     return x.astype(np.float32) - CAFFE_MASK
 
 
-def compute_resize_scale(image_shape, min_side=800, max_side=1333):
-    (rows, cols, _) = image_shape
-    smallest_side = min(rows, cols)
+def _get_resize_scale(size, min_side=800, max_side=1333):
+    width, height = size
+    smallest_side = min(width, height)
+    largest_side = max(width, height)
 
     scale = min_side / smallest_side
-    largest_side = max(rows, cols)
     if largest_side * scale > max_side:
         scale = max_side / largest_side
 
     return scale
 
 
-def resize_image(img, min_side=800, max_side=1333):
-    scale = compute_resize_scale(img.shape, min_side=min_side, max_side=max_side)
-    img = cv2.resize(img, None, fx=scale, fy=scale)
-    return img, scale
-
-
 def preprocess_image(
         image: Image.Image, min_side=800, max_side=1333,
 ):
-    image = read_image_bgr(image)
-    image = _preprocess_image(image)
-    image, scale = resize_image(image, min_side=min_side, max_side=max_side)
-    return image, scale
+    width, height = image.size
+    scale = _get_resize_scale((width, height), min_side=min_side, max_side=max_side)
+    new_width, new_height = map(lambda x: int(x * scale), (width, height))
+    data = _read_bgr_data(image.resize((new_width, new_height)))
+    data = _preprocess_image(data)
+    return data, scale
 
 
 FILE_URLS = {

requirements.txt

@@ -1,5 +1,4 @@
 pillow
-opencv-python
 numpy
 gradio==3.16.2
 huggingface_hub