entries become suddenly empty

handcrafted_solution.py

@@ -110,13 +110,12 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
 
     vertices = np.concatenate([apex_centroids, eave_end_point_centroids])
 
-
     scale = 1
     vertex_size = np.zeros(vertices.shape[0])
     for i, coords in enumerate(vertices):
         # coords = np.round(coords).astype(np.uint32)
-        radius = 25#np.clip(int(max_depth//2 + depth_np[coords[1], coords[0]]), 10, 30)#int(np.clip(max_depth - depth_np[coords[1], coords[0]], 10, 20))
-        vertex_size[i] = (scale*radius) ** 2 # because we are using squared distances
+        radius = 25  # np.clip(int(max_depth//2 + depth_np[coords[1], coords[0]]), 10, 30)#int(np.clip(max_depth - depth_np[coords[1], coords[0]], 10, 20))
+        vertex_size[i] = (scale * radius) ** 2  # because we are using squared distances
 
     for edge_class in ['eave', 'ridge', 'rake', 'valley', 'flashing', 'step_flashing']:
         if len(vertices) < 2:
@@ -124,18 +123,13 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
         edge_color = np.array(gestalt_color_mapping[edge_class])
 
         mask = cv2.inRange(gest_seg_np,
-                           edge_color-color_range,
-                           edge_color+color_range)
+                           edge_color - color_range,
+                           edge_color + color_range)
         mask = cv2.morphologyEx(mask,
                                 cv2.MORPH_DILATE, np.ones((3, 3)), iterations=1)
 
-
-
         if np.any(mask):
 
-
-
-
             rho = 1  # distance resolution in pixels of the Hough grid
             theta = np.pi / 180  # angular resolution in radians of the Hough grid
             threshold = 20  # minimum number of votes (intersections in Hough grid cell)
@@ -144,11 +138,10 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
 
             # Run Hough on edge detected image
             # Output "lines" is an array containing endpoints of detected line segments
-            cv2.GaussianBlur(mask, (11,11), 0, mask)
+            cv2.GaussianBlur(mask, (11, 11), 0, mask)
             lines = cv2.HoughLinesP(mask, rho, theta, threshold, np.array([]),
                                     min_line_length, max_line_gap)
 
-
             edges = []
 
             if lines is None:
@@ -156,7 +149,7 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
 
             line_directions = np.zeros((len(lines), 2))
             for line_idx, line in enumerate(lines):
-                for x1,y1,x2,y2 in line:
+                for x1, y1, x2, y2 in line:
                     extend = 35
                     if x1 < x2:
                         x1, y1, x2, y2 = x2, y2, x1, y1
@@ -166,16 +159,13 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
 
                     direction = extend * direction
 
-                    x1,y1 = (-direction + (x1, y1)).astype(np.int32)
-                    x2,y2 = (+ direction + (x2, y2)).astype(np.int32)
+                    x1, y1 = (-direction + (x1, y1)).astype(np.int32)
+                    x2, y2 = (+ direction + (x2, y2)).astype(np.int32)
 
                     edges.append((x1, y1, x2, y2))
 
-
-
-
             edges = np.array(edges)
-            if len(edges) <1:
+            if len(edges) < 1:
                 continue
             # calculate the distances between the vertices and the edge ends
             begin_distances = cdist(vertices, edges[:, :2], metric="sqeuclidean")
@@ -187,7 +177,6 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
             in_range_connected_mask = np.logical_and(np.any(begin_in_range_mask, axis=0),
                                                      np.any(end_in_range_mask, axis=0))
 
-
             # where both ends are in range
             begin_in_range_mask = np.logical_and(begin_in_range_mask, in_range_connected_mask)
             end_in_range_mask = np.logical_and(end_in_range_mask, in_range_connected_mask)
@@ -201,7 +190,6 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
             begin_candidates = begin_candidates[:, sorted_begin_indices]
             end_candidates = end_candidates[:, sorted_end_indices]
 
-
             # create all possible connections between begin and end candidates that correspond to a line
             grouped_begins = np.split(begin_candidates[0], np.unique(begin_candidates[1], return_index=True)[1][1:])
             grouped_ends = np.split(end_candidates[0], np.unique(end_candidates[1], return_index=True)[1][1:])
@@ -231,12 +219,13 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
 
             # precalculate the possible direction vectors
             possible_direction_vectors = vertices[possible_connections[0]] - vertices[possible_connections[1]]
-            possible_direction_vectors = possible_direction_vectors / np.linalg.norm(possible_direction_vectors, axis=1)[:, np.newaxis]
+            possible_direction_vectors = possible_direction_vectors / np.linalg.norm(possible_direction_vectors,
+                                                                                     axis=1)[:, np.newaxis]
 
             owned_lines_per_possible_connections = [list() for i in range(possible_connections.shape[1])]
 
             # assign lines to possible connections
-            for line_idx, i,j in zip(line_idx_list, begin_vertex_list, end_vertex_list):
+            for line_idx, i, j in zip(line_idx_list, begin_vertex_list, end_vertex_list):
                 if i == j:
                     continue
                 i, j = min(i, j), max(i, j)
@@ -247,7 +236,8 @@ def get_vertices_and_edges_from_segmentation(gest_seg_np, edge_th=50.0):
 
             # check if the lines are in the same direction as the possible connection
             for fitted_line_idx, owned_lines_per_possible_connection in enumerate(owned_lines_per_possible_connections):
-                line_deviations = np.abs(np.dot(line_directions[owned_lines_per_possible_connection], possible_direction_vectors[fitted_line_idx]))
+                line_deviations = np.abs(np.dot(line_directions[owned_lines_per_possible_connection],
+                                                possible_direction_vectors[fitted_line_idx]))
                 if np.any(line_deviations > deviation_threshold):
                     connections.append(possible_connections[:, fitted_line_idx])
 
@@ -350,6 +340,9 @@ def prune_not_connected(all_3d_vertices, connections_3d):
 
 def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
     good_entry = convert_entry_to_human_readable(entry)
+    if 'gestalt' not in good_entry or 'depthcm' not in good_entry or 'K' not in good_entry or 'R' not in good_entry or 't' not in good_entry:
+        print('Missing required fields in the entry')
+        return (good_entry['__key__'], *empty_solution())
     vert_edge_per_image = {}
     for i, (gest, depth, K, R, t) in enumerate(zip(good_entry['gestalt'],
                                                    good_entry['depthcm'],

test_solution.ipynb

@@ -0,0 +1,204 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-05-26T20:37:02.290189Z",
+     "start_time": "2024-05-26T20:36:59.535549Z"
+    }
+   },
+   "source": [
+    "from pprint import pprint\n",
+    "\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "import hoho\n",
+    "from hoho import compute_WED\n",
+    "from hoho import vis\n",
+    "from hoho.vis import line\n",
+    "\n",
+    "from scipy.spatial.distance import cdist\n",
+    "import cv2\n",
+    "hoho.LOCAL_DATADIR = hoho.setup(\"../data\")\n",
+    "from handcrafted_solution import predict"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using ..\\data as the data directory (we are running locally)\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "# from ..s23dr-hoho-competition import handcrafted_solution",
+   "id": "4e9860e9763942ac"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "679a2b4f8e46a308"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-26T20:29:23.028196Z",
+     "start_time": "2024-05-26T20:29:23.024659Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "",
+   "id": "1146933b5aabff1c",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-26T20:37:02.297233Z",
+     "start_time": "2024-05-26T20:37:02.292201Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "split = \"val\"\n",
+    "\n",
+    "dataset = hoho.get_dataset(decode=None, split=split)"
+   ],
+   "id": "55c712587c694e96",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "7daaea23230c7bb6"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-26T20:40:18.110964Z",
+     "start_time": "2024-05-26T20:39:24.449965Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from tqdm import tqdm\n",
+    "\n",
+    "for i, sample in tqdm(enumerate(dataset)):\n",
+    "    # if i > 170:\n",
+    "    predict(sample, visualize=False)"
+   ],
+   "id": "f36ee7b8f0427f72",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2it [00:02,  1.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Not enough vertices or connections in image 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "10it [00:15,  1.45s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Not enough vertices or connections in image 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "29it [00:47,  1.22s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Not enough vertices or connections in image 4\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "33it [00:52,  1.60s/it]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
+      "Cell \u001B[1;32mIn[4], line 5\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mtqdm\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m tqdm\n\u001B[0;32m      3\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i, sample \u001B[38;5;129;01min\u001B[39;00m tqdm(\u001B[38;5;28menumerate\u001B[39m(dataset)):\n\u001B[0;32m      4\u001B[0m     \u001B[38;5;66;03m# if i > 170:\u001B[39;00m\n\u001B[1;32m----> 5\u001B[0m     predict(sample, visualize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
+      "File \u001B[1;32m~\\edu\\sem6\\cv\\huggingface\\s23dr-hoho-competition\\handcrafted_solution.py:342\u001B[0m, in \u001B[0;36mpredict\u001B[1;34m(entry, visualize)\u001B[0m\n\u001B[0;32m    341\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpredict\u001B[39m(entry, visualize\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Tuple[np\u001B[38;5;241m.\u001B[39mndarray, List[\u001B[38;5;28mint\u001B[39m]]:\n\u001B[1;32m--> 342\u001B[0m     good_entry \u001B[38;5;241m=\u001B[39m convert_entry_to_human_readable(entry)\n\u001B[0;32m    343\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgestalt\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m good_entry \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdepthcm\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m good_entry \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mK\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m good_entry \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mR\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m good_entry \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m good_entry:\n\u001B[0;32m    344\u001B[0m         \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mMissing required fields in the entry\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
+      "File \u001B[1;32m~\\edu\\sem6\\cv\\huggingface\\s23dr-hoho-competition\\handcrafted_solution.py:95\u001B[0m, in \u001B[0;36mconvert_entry_to_human_readable\u001B[1;34m(entry)\u001B[0m\n\u001B[0;32m     85\u001B[0m         out[k] \u001B[38;5;241m=\u001B[39m v\n\u001B[0;32m     86\u001B[0m         \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[0;32m     87\u001B[0m     \u001B[38;5;28;01mmatch\u001B[39;00m k:\n\u001B[0;32m     88\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpoints3d\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     89\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m read_points3D_binary(fid\u001B[38;5;241m=\u001B[39mio\u001B[38;5;241m.\u001B[39mBytesIO(v))\n\u001B[0;32m     90\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcameras\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     91\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m read_cameras_binary(fid\u001B[38;5;241m=\u001B[39mio\u001B[38;5;241m.\u001B[39mBytesIO(v))\n\u001B[0;32m     92\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mimages\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     93\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m read_images_binary(fid\u001B[38;5;241m=\u001B[39mio\u001B[38;5;241m.\u001B[39mBytesIO(v))\n\u001B[0;32m     94\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124made20k\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m|\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgestalt\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[1;32m---> 95\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m [PImage\u001B[38;5;241m.\u001B[39mopen(io\u001B[38;5;241m.\u001B[39mBytesIO(x))\u001B[38;5;241m.\u001B[39mconvert(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mRGB\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m v]\n\u001B[0;32m     96\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdepthcm\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     97\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m [PImage\u001B[38;5;241m.\u001B[39mopen(io\u001B[38;5;241m.\u001B[39mBytesIO(x)) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m entry[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdepthcm\u001B[39m\u001B[38;5;124m'\u001B[39m]]\n\u001B[0;32m     98\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m out\n",
+      "File \u001B[1;32m~\\edu\\sem6\\cv\\huggingface\\s23dr-hoho-competition\\handcrafted_solution.py:95\u001B[0m, in \u001B[0;36m<listcomp>\u001B[1;34m(.0)\u001B[0m\n\u001B[0;32m     85\u001B[0m         out[k] \u001B[38;5;241m=\u001B[39m v\n\u001B[0;32m     86\u001B[0m         \u001B[38;5;28;01mcontinue\u001B[39;00m\n\u001B[0;32m     87\u001B[0m     \u001B[38;5;28;01mmatch\u001B[39;00m k:\n\u001B[0;32m     88\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpoints3d\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     89\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m read_points3D_binary(fid\u001B[38;5;241m=\u001B[39mio\u001B[38;5;241m.\u001B[39mBytesIO(v))\n\u001B[0;32m     90\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcameras\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     91\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m read_cameras_binary(fid\u001B[38;5;241m=\u001B[39mio\u001B[38;5;241m.\u001B[39mBytesIO(v))\n\u001B[0;32m     92\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mimages\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     93\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m read_images_binary(fid\u001B[38;5;241m=\u001B[39mio\u001B[38;5;241m.\u001B[39mBytesIO(v))\n\u001B[0;32m     94\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124made20k\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m|\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgestalt\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[1;32m---> 95\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m [PImage\u001B[38;5;241m.\u001B[39mopen(io\u001B[38;5;241m.\u001B[39mBytesIO(x))\u001B[38;5;241m.\u001B[39mconvert(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mRGB\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m v]\n\u001B[0;32m     96\u001B[0m         \u001B[38;5;28;01mcase\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdepthcm\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m     97\u001B[0m             out[k] \u001B[38;5;241m=\u001B[39m [PImage\u001B[38;5;241m.\u001B[39mopen(io\u001B[38;5;241m.\u001B[39mBytesIO(x)) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m entry[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdepthcm\u001B[39m\u001B[38;5;124m'\u001B[39m]]\n\u001B[0;32m     98\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m out\n",
+      "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\PIL\\Image.py:922\u001B[0m, in \u001B[0;36mImage.convert\u001B[1;34m(self, mode, matrix, dither, palette, colors)\u001B[0m\n\u001B[0;32m    874\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mconvert\u001B[39m(\n\u001B[0;32m    875\u001B[0m     \u001B[38;5;28mself\u001B[39m, mode\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, matrix\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, dither\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, palette\u001B[38;5;241m=\u001B[39mPalette\u001B[38;5;241m.\u001B[39mWEB, colors\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m256\u001B[39m\n\u001B[0;32m    876\u001B[0m ):\n\u001B[0;32m    877\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    878\u001B[0m \u001B[38;5;124;03m    Returns a converted copy of this image. For the \"P\" mode, this\u001B[39;00m\n\u001B[0;32m    879\u001B[0m \u001B[38;5;124;03m    method translates pixels through the palette.  If mode is\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    919\u001B[0m \u001B[38;5;124;03m    :returns: An :py:class:`~PIL.Image.Image` object.\u001B[39;00m\n\u001B[0;32m    920\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 922\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mload()\n\u001B[0;32m    924\u001B[0m     has_transparency \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtransparency\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39minfo\n\u001B[0;32m    925\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m mode \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmode \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mP\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[0;32m    926\u001B[0m         \u001B[38;5;66;03m# determine default mode\u001B[39;00m\n",
+      "File \u001B[1;32m~\\anaconda3\\Lib\\site-packages\\PIL\\WebPImagePlugin.py:168\u001B[0m, in \u001B[0;36mWebPImageFile.load\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    166\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfp \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_exclusive_fp:\n\u001B[0;32m    167\u001B[0m             \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfp\u001B[38;5;241m.\u001B[39mclose()\n\u001B[1;32m--> 168\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfp \u001B[38;5;241m=\u001B[39m BytesIO(data)\n\u001B[0;32m    169\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtile \u001B[38;5;241m=\u001B[39m [(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mraw\u001B[39m\u001B[38;5;124m\"\u001B[39m, (\u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0\u001B[39m) \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msize, \u001B[38;5;241m0\u001B[39m, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrawmode)]\n\u001B[0;32m    171\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mload()\n",
+      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
+     ]
+    }
+   ],
+   "execution_count": 4
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
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+   "mimetype": "text/x-python",
+   "name": "python",
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