yolov4: fix typo

Author: hhk7734

benchmarks.py

@@ -83,7 +83,7 @@ def run_model(x):
     original_image = cv2.imread(FLAGS.image)
     original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
     original_image_size = original_image.shape[:2]
-    image_data = utils.image_preporcess(np.copy(original_image), [FLAGS.size, FLAGS.size])
+    image_data = utils.image_preprocess(np.copy(original_image), [FLAGS.size, FLAGS.size])
     image_data = image_data[np.newaxis, ...].astype(np.float32)
     img_raw = tf.image.decode_image(
         open(FLAGS.image, 'rb').read(), channels=3)

convert_tflite.py

@@ -22,7 +22,7 @@ def representative_data_gen():
     if os.path.exists(fimage[input_value]):
       original_image=cv2.imread(fimage[input_value])
       original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
-      image_data = utils.image_preporcess(np.copy(original_image), [FLAGS.input_size, FLAGS.input_size])
+      image_data = utils.image_preprocess(np.copy(original_image), [FLAGS.input_size, FLAGS.input_size])
       img_in = image_data[np.newaxis, ...].astype(np.float32)
       print(input_value)
       yield [img_in]

convert_trt.py

@@ -23,7 +23,7 @@ def representative_data_gen():
     if os.path.exists(fimage[input_value]):
       original_image=cv2.imread(fimage[input_value])
       original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
-      image_data = utils.image_preporcess(np.copy(original_image), [FLAGS.input_size, FLAGS.input_size])
+      image_data = utils.image_preprocess(np.copy(original_image), [FLAGS.input_size, FLAGS.input_size])
       img_in = image_data[np.newaxis, ...].astype(np.float32)
       batched_input = tf.constant(img_in)
       print(input_value)

core/dataset.py

@@ -170,7 +170,7 @@ def parse_annotation(self, annotation):
             image, bboxes = self.random_translate(np.copy(image), np.copy(bboxes))
 
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        image, bboxes = utils.image_preporcess(np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes))
+        image, bboxes = utils.image_preprocess(np.copy(image), [self.train_input_size, self.train_input_size], np.copy(bboxes))
         return image, bboxes
 
     def bbox_iou(self, boxes1, boxes2):

core/utils.py

@@ -143,7 +143,7 @@ def get_anchors(anchors_path, tiny=False):
         return anchors.reshape(3, 3, 2)
 
 
-def image_preporcess(image, target_size, gt_boxes=None):
+def image_preprocess(image, target_size, gt_boxes=None):
 
     ih, iw    = target_size
     h,  w, _  = image.shape

detect.py

@@ -37,7 +37,7 @@ def main(_argv):
     original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
     original_image_size = original_image.shape[:2]
 
-    image_data = utils.image_preporcess(np.copy(original_image), [input_size, input_size])
+    image_data = utils.image_preprocess(np.copy(original_image), [input_size, input_size])
     image_data = image_data[np.newaxis, ...].astype(np.float32)
     if FLAGS.framework == 'tf':
         input_layer = tf.keras.layers.Input([input_size, input_size, 3])

detectvideo.py

@@ -86,7 +86,7 @@ def main(_argv):
         else:
             raise ValueError("No image! Try with another video format")
         frame_size = frame.shape[:2]
-        image_data = utils.image_preporcess(np.copy(frame), [input_size, input_size])
+        image_data = utils.image_preprocess(np.copy(frame), [input_size, input_size])
         image_data = image_data[np.newaxis, ...].astype(np.float32)
         prev_time = time.time()
 

evaluate.py

@@ -111,7 +111,7 @@ def main(_argv):
             predict_result_path = os.path.join(predicted_dir_path, str(num) + '.txt')
             # Predict Process
             image_size = image.shape[:2]
-            image_data = utils.image_preporcess(np.copy(image), [INPUT_SIZE, INPUT_SIZE])
+            image_data = utils.image_preprocess(np.copy(image), [INPUT_SIZE, INPUT_SIZE])
             image_data = image_data[np.newaxis, ...].astype(np.float32)
 
             if FLAGS.framework == "tf":