Blah

app.py

@@ -2,6 +2,8 @@ import numpy as np
 from PIL import Image
 import gradio as gr
 from ultralytics import YOLO
+from ultralytics.yolo.utils.ops import scale_image
+import cv2
 
 # Load the YOLO model
 m_raw_model = YOLO("M-Raw.pt")
@@ -22,10 +24,19 @@ def snap(image, model, conf, iou):
         results = s_raw_model(image, conf=conf, iou=iou)
 
     # Convert the results list into an output image
-    print(results)
+    for result in results:
+        classes = result.boxes.cls.cpu().numpy()
+        probs = result.boxes.conf.cpu().numpy()
+        boxes = result.boxes.xyxy[0].cpu().numpy()
+
+        for i in range(len(boxes)):
+            x1, y1, x2, y2 = boxes[i]
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(image, f"{classes[i]} {probs[i]:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
     # Convert the resulting image to a PIL image
-    resulting_image = Image.fromarray(resulting_image)
+    resulting_image = Image.fromarray(image)
 
     # Get the labels
     labels = results.pandas().xyxy[0]["name"].values
@@ -38,7 +49,7 @@ def snap(image, model, conf, iou):
 
 demo = gr.Interface(
     snap,
-    [gr.Image(source="webcam", tool=None, streaming=True), gr.inputs.Radio(["M-Raw", "S-Raw", "N-Raw"]), gr.Slider(0, 1, value=0.6, label="Classifier Confidence Threshold"), gr.Slider(0, 1, value=0.7, label="IoU Threshold")],
+    [gr.Image(source="webcam", tool=None, streaming=True), gr.inputs.Radio(["M-Raw", "S-Raw", "N-Raw"], value="M-Raw"), gr.Slider(0, 1, value=0.6, label="Classifier Confidence Threshold"), gr.Slider(0, 1, value=0.7, label="IoU Threshold")],
     ["image"],
     title="Baybayin Instance Detection"
 )

requirements.txt

@@ -1,4 +1,5 @@
 numpy
 Pillow
 ultralytics==8.0.21
-gradio
+gradio
+opencv-python