mh

app.py

@@ -15,36 +15,25 @@ 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(im, size=640):
+#     outputs = model.predict(source=image_path)
+#     results = outputs[0].cpu().numpy()
+#     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 cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+def show_preds_image(im):
     #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"
+    return results.render()[0]
 
 inputs_image = [
     gr.components.Image(type="filepath", label="Input Image"),