image addded

Clip_model_notebook.ipynb

@@ -2446,38 +2446,6 @@
       "source": [
         "plt.imshow(display_images(rgb_image,detections=predictions,prompt='Cars'))"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "y1DuKm34myry"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "['bed', 'door', 'window', 'cars']"
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "a = 'bed ,door, window, cars '\n",
-        "v = a.split(',')\n",
-        "v = list(map(lambda x: x.strip(),v))\n",
-        "v"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
     }
   ],
   "metadata": {

app.py

@@ -49,7 +49,7 @@ def detect_using_clip(image,prompts=[],threshould=0.4):
         # extract countours from the image
         lbl_0 = label(predicted_image)
         props = regionprops(lbl_0)
-        model_detections[prompt] = [rescale_bbox(prop.bbox,orig_image_shape=image.shape[:2],model_shape=predicted_image.shape[0]) for prop in props]
+        model_detections[prompt.lower()] = [rescale_bbox(prop.bbox,orig_image_shape=image.shape[:2],model_shape=predicted_image.shape[0]) for prop in props]
 
     return model_detections 
 
@@ -59,7 +59,7 @@ def visualize_images(image,detections,prompt):
     if prompt not in detections.keys():
         print("prompt not in query ..")
         return image_copy
-    for bbox in detections[prompt]:
+    for bbox in detections[prompt.lower()]:
         cv2.rectangle(image_copy, (int(bbox[1]), int(bbox[0])), (int(bbox[3]), int(bbox[2])), (255, 0, 0), 2)
         cv2.putText(image_copy,str(prompt),(int(bbox[1]), int(bbox[0])),cv2.FONT_HERSHEY_SIMPLEX, 2, 255)
     return image_copy