Update model_3.py

model_3.py

@@ -1,4 +1,4 @@
-# model_1.py
+# model_3.py
 import os
 import cv2
 import numpy as np
@@ -42,10 +42,10 @@ if 'StatefulPartitionedCall' in outname:
 else:
     boxes_idx, classes_idx, scores_idx = 0, 1, 2
 
-def perform_detection(image):
+def perform_detection(image, target_size=(640, 640)):
     imH, imW, _ = image.shape
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    image_resized = cv2.resize(image_rgb, (width, height))
+    image_resized = cv2.resize(image_rgb, target_size)
     input_data = np.expand_dims(image_resized, axis=0)
 
     if floating_model:
@@ -66,7 +66,7 @@ def perform_detection(image):
             ymax = int(min(imH, (boxes[i][2] * imH)))
             xmax = int(min(imW, (boxes[i][3] * imW)))
 
-            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (10, 255, 0), 2)
+            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)  # Red color for bounding box
             object_name = labels[int(classes[i])]
             label = '%s: %d%%' % (object_name, int(scores[i] * 100))
             labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2)
@@ -79,7 +79,7 @@ def perform_detection(image):
 
 def detect_image(input_image):
     image = np.array(input_image)
-    result_image = perform_detection(image)
+    result_image = perform_detection(image, target_size=(640, 640))
     return Image.fromarray(result_image)
 
 def detect_video(input_video):
@@ -91,7 +91,7 @@ def detect_video(input_video):
         if not ret:
             break
 
-        result_frame = perform_detection(frame)
+        result_frame = perform_detection(frame, target_size=(640, 640))
         frames.append(result_frame)
 
     cap.release()