Upload preprocess.py

preprocess.py

@@ -0,0 +1,53 @@
+import numpy as np
+import cv2
+
+
+# This function is modified from the following code snippet:
+# https://github.com/StanislasBertrand/RetinaFace-tf2/blob/5f68ce8130889384cb8aca937a270cea4ef2d020/retinaface.py#L49-L74
+def resize_image(img, scales, allow_upscaling):
+    img_h, img_w = img.shape[0:2]
+    target_size = scales[0]
+    max_size = scales[1]
+
+    if img_w > img_h:
+        im_size_min, im_size_max = img_h, img_w
+    else:
+        im_size_min, im_size_max = img_w, img_h
+
+    im_scale = target_size / float(im_size_min)
+    if not allow_upscaling:
+        im_scale = min(1.0, im_scale)
+
+    if np.round(im_scale * im_size_max) > max_size:
+        im_scale = max_size / float(im_size_max)
+
+    if im_scale != 1.0:
+        img = cv2.resize(
+            img,
+            None,
+            None,
+            fx=im_scale,
+            fy=im_scale,
+            interpolation=cv2.INTER_LINEAR
+        )
+
+    return img, im_scale
+
+
+# This function is modified from the following code snippet:
+# https://github.com/StanislasBertrand/RetinaFace-tf2/blob/5f68ce8130889384cb8aca937a270cea4ef2d020/retinaface.py#L76-L96
+def preprocess_image(img, allow_upscaling):
+    pixel_means = np.array([0.0, 0.0, 0.0], dtype=np.float32)
+    pixel_stds = np.array([1.0, 1.0, 1.0], dtype=np.float32)
+    pixel_scale = float(1.0)
+    scales = [1024, 1980]
+
+    img, im_scale = resize_image(img, scales, allow_upscaling)
+    img = img.astype(np.float32)
+    im_tensor = np.zeros((1, img.shape[0], img.shape[1], img.shape[2]), dtype=np.float32)
+
+    # Make image scaling + BGR2RGB conversion + transpose (N,H,W,C) to (N,C,H,W)
+    for i in range(3):
+        im_tensor[0, :, :, i] = (img[:, :, 2 - i] / pixel_scale - pixel_means[2 - i]) / pixel_stds[2 - i]
+
+    return im_tensor, img.shape[0:2], im_scale