Fix error for no objects

app.py

@@ -1,3 +1,4 @@
+import logging
 import os
 
 import gradio as gr
@@ -9,6 +10,11 @@ from inference.face_detector import StatRetinaFaceDetector
 from inference.model_pipeline import VSNetModelPipeline
 from inference.onnx_model import ONNXModel
 
+logging.basicConfig(
+    format='%(asctime)s %(levelname)-8s %(message)s',
+    level=logging.INFO,
+    datefmt='%Y-%m-%d %H:%M:%S')
+
 MODEL_IMG_SIZE = 256
 def load_model():
     REPO_ID = "Podtekatel/JJBAGAN"

inference/face_detector.py

@@ -96,7 +96,9 @@ class StatRetinaFaceDetector(FaceDetector):
     def detect_crops(self, img, *args, **kwargs):
         faces = RetinaFace.detect_faces(img, model=self.model)
         crops = []
-        for naem, face in faces.items():
+        if len(faces) == 0:
+            faces = {}
+        for name, face in faces.items():
             x1, y1, x2, y2 = face['facial_area']
             crop = np.array([x1, y1, x2, y2])
             crops.append(crop)

inference/model_pipeline.py

@@ -1,3 +1,4 @@
+import logging
 import time
 
 import cv2
@@ -23,7 +24,6 @@ class VSNetModelPipeline:
 
         Y, X = np.ogrid[:h, :w]
         dist_from_center = np.sqrt((X - center[0]) ** 2 + (Y - center[1]) ** 2)
-        print(dist_from_center.max())
         dist_from_center = np.clip(dist_from_center, a_min=0, a_max=max(h / 2, w / 2))
         dist_from_center = 1 - dist_from_center / np.max(dist_from_center)
         if power is not None:
@@ -86,7 +86,7 @@ class VSNetModelPipeline:
             out_faces = out_faces.transpose(0, 2, 3, 1)
             out_faces = np.clip(out_faces * 255, 0, 255).astype(np.uint8)
             end_time = time.time()
-            print(f'Face FPS {1 / (end_time - start_time)}')
+            logging.info(f'Face FPS {1 / (end_time - start_time)}')
         else:
             out_faces = []
             img = self.resize_size(img, size=self.no_detected_resize)
@@ -100,7 +100,7 @@ class VSNetModelPipeline:
         full_image = full_image.transpose(0, 2, 3, 1)
         full_image = np.clip(full_image * 255, 0, 255).astype(np.uint8)
         end_time = time.time()
-        print(f'Background FPS {1 / (end_time - start_time)}')
+        logging.info(f'Background FPS {1 / (end_time - start_time)}')
 
         result_image = self.merge_crops(out_faces, coords, full_image[0])
         return result_image