testing

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import cv2
 import requests
 import os
+from PIL import Image
 
 import torch
 import ultralytics
@@ -14,36 +15,44 @@ model.conf = 0.20  # NMS confidence threshold
 
 path  = [['img/test-image.jpg'], ['img/test-image-2.jpg']]
 
-def show_preds_image(image_path):
-    image = cv2.imread(image_path)
-    # outputs = model(source=image_path)
-    # results = outputs[0].cpu().numpy()
-    results = model(image_path)
-    results.xyxy[0]  # img1 predictions (tensor)
-    results.pandas().xyxy[0]  # img1 predictions (pandas)
-    predictions = results.pred[0]
-    boxes = predictions[:, :4] # x1, y1, x2, y2
-    scores = predictions[:, 4]
-    categories = predictions[:, 5]
-
-    # for i, det in enumerate(results.boxes.xyxy):
-    #     cv2.rectangle(
-    #         image,
-    #         (int(det[0]), int(det[1])),
-    #         (int(det[2]), int(det[3])),
-    #         color=(0, 0, 255),
-    #         thickness=2,
-    #         lineType=cv2.LINE_AA
-    #     )
-    return results.show()
+# def show_preds_image(image_path):
+#     image = cv2.imread(image_path)
+#     # outputs = model(source=image_path)
+#     # results = outputs[0].cpu().numpy()
+#     results = model(image_path)
+#     results.xyxy[0]  # img1 predictions (tensor)
+#     results.pandas().xyxy[0]  # img1 predictions (pandas)
+#     predictions = results.pred[0]
+#     boxes = predictions[:, :4] # x1, y1, x2, y2
+#     scores = predictions[:, 4]
+#     categories = predictions[:, 5]
 
+#     # for i, det in enumerate(results.boxes.xyxy):
+#     #     cv2.rectangle(
+#     #         image,
+#     #         (int(det[0]), int(det[1])),
+#     #         (int(det[2]), int(det[3])),
+#     #         color=(0, 0, 255),
+#     #         thickness=2,
+#     #         lineType=cv2.LINE_AA
+#     #     )
+#     return results.show()
 
+def show_preds_image(im, size=640):
+    g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize
+
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    results.save()
+    os.system("ls")
+    return "out.png"
 
 inputs_image = [
     gr.components.Image(type="filepath", label="Input Image"),
 ]
 outputs_image = [
-    gr.components.Image(type="pil", label="Output Image"),
+    gr.components.Image(type="file", label="Output Image"),
 ]
 interface_image = gr.Interface(
     fn=show_preds_image,