Fixed the other branches of crop_resize function to support various image formats and improve bounding box handling

Author: p4nzer96

models/utils/detect_face.py

@@ -307,36 +307,72 @@ def imresample(img, sz):
 
 
 def crop_resize(img, box, image_size):
+    """
+    box: (x1, y1, x2, y2) in pixel coords, x2/y2 exclusive-style is fine too (we resize anyway).
+    img: numpy HWC, torch HWC or CHW, or PIL Image
+    """
+
+    x1, y1, x2, y2 = map(int, box)
+    w = max(1, x2 - x1)
+    h = max(1, y2 - y1)
+
+    s = max(w, h)
+    cx = x1 + w / 2.0
+    cy = y1 + h / 2.0
+
+    # square window [x0, x0+s), [y0, y0+s)
+    x0 = int(round(cx - s / 2.0))
+    y0 = int(round(cy - s / 2.0))
+
     if isinstance(img, np.ndarray):
-        # box[1] -> y1
-        # box[3] -> y2
-        # box[0] -> x1
-        # box[2] -> x2
-        h = box[3] - box[1]
-        w = box[2] - box[0]
-        yc = box[1] + h // 2
-        xc = box[0] + w // 2
-        intermediate_size = max(w, h)
-        y0 = max(0, yc - intermediate_size // 2)
-        y1 = min(img.shape[0], yc + intermediate_size // 2)
-        x0 = max(0, xc - intermediate_size // 2)
-        x1 = min(img.shape[1], xc + intermediate_size // 2)
-        img = img[y0:y1, x0:x1]
-
-        out = cv2.resize(
-            img,
-            (image_size, image_size),
-            interpolation=cv2.INTER_AREA
-        ).copy()
+        H, W = img.shape[:2]
     elif isinstance(img, torch.Tensor):
-        img = img[box[1]:box[3], box[0]:box[2]]
-        out = imresample(
-            img.permute(2, 0, 1).unsqueeze(0).float(),
-            (image_size, image_size)
-        ).byte().squeeze(0).permute(1, 2, 0)
+        # accept HWC or CHW
+        if img.ndim != 3:
+            raise ValueError("torch img must be 3D (HWC or CHW)")
+        if img.shape[0] in (1, 3, 4) and img.shape[2] not in (1, 3, 4):
+            # CHW
+            C, H, W = img.shape
+            chw = True
+        else:
+            # HWC
+            H, W, C = img.shape
+            chw = False
     else:
-        out = img.crop(box).copy().resize((image_size, image_size), Image.BILINEAR)
-    return out
+        # PIL
+        W, H = img.size
+
+    # shift window to stay inside image (keeps square)
+    x0 = min(max(0, x0), max(0, W - s))
+    y0 = min(max(0, y0), max(0, H - s))
+    x1n, y1n = x0 + s, y0 + s
+
+    if isinstance(img, np.ndarray):
+        crop = img[y0:y1n, x0:x1n]
+        return cv2.resize(crop, (image_size, image_size), interpolation=cv2.INTER_AREA).copy()
+
+    if isinstance(img, torch.Tensor):
+        if chw:
+            crop = img[:, y0:y1n, x0:x1n]
+        else:
+            crop = img[y0:y1n, x0:x1n, :]
+
+        # simplest: use torch.nn.functional.interpolate on float
+        import torch.nn.functional as F
+        if chw:
+            crop_f = crop.unsqueeze(0).float()
+        else:
+            crop_f = crop.permute(2, 0, 1).unsqueeze(0).float()
+
+        out = F.interpolate(crop_f, size=(image_size, image_size), mode="area")
+        out = out.squeeze(0)
+        if not chw:
+            out = out.permute(1, 2, 0)
+        return out.byte()
+
+    # PIL
+    crop = img.crop((x0, y0, x1n, y1n))
+    return crop.resize((image_size, image_size), Image.BILINEAR)
 
 
 def save_img(img, path):