feat: add prediction with full image

main.py

@@ -74,10 +74,21 @@ def animal_detect_and_classify(img_path):
             # Make predictions using the classification model
             prediction = classification_model.predict(inp_array)
             # Map predictions to labels
-            threshold = 0.75
+            threshold = 0.66
             predicted_labels = [labels[np.argmax(pred)] if np.max(pred) >= threshold else "animal" for pred in prediction]
             print(predicted_labels)
             combined_results.append(((x1, y1, x2, y2), predicted_labels))
+        y2, x2, _ = img.shape
+        detect_img = img[0:y2, 0:x2]
+        detect_img = cv2.cvtColor(detect_img, cv2.COLOR_BGR2RGB)
+        detect_img = cv2.resize(detect_img, (224, 224))
+        inp_array = np.array(detect_img)
+        inp_array = inp_array.reshape((-1, 224, 224, 3))
+        inp_array = tf.keras.applications.efficientnet.preprocess_input(inp_array)
+        prediction = classification_model.predict(inp_array)
+        threshold = 0.66
+        predicted_labels = [labels[np.argmax(pred)] if np.max(pred) >= threshold else "unknown" for pred in prediction]
+        combined_results.append(((0, 0, x2, y2), predicted_labels))
 
     return combined_results
 
@@ -101,6 +112,8 @@ def plot_detected_rectangles(image, detections, output_path):
 
     # Iterate over each detected rectangle and its corresponding class name
     for rectangle, class_names in detections:
+        if class_names[0] == "unknown":
+            continue
         # Extract the coordinates of the rectangle
         x1, y1, x2, y2 = rectangle