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import math
from shapely.geometry import Polygon
from util.geom_utils import poly_iou
def calculate_AP(valid_results, ground_truths, confidence_final):
ground_truths_copy = copy.deepcopy(ground_truths)
all_preds = []
for image_id, image_pred in valid_results.items():
for i in range(len(image_pred["points"])):
pred = {}
pred["score"] = image_pred["scores"][i].item()
pred["point"] = tuple(image_pred["points"][i].tolist())
pred["size"] = tuple(image_pred["size"].tolist())
pred["image_id"] = image_id.item()
all_preds.append(pred)
all_preds = sorted(all_preds, key=lambda x: x["score"], reverse=True)
all_preds = [pred for pred in all_preds if pred["score"] > confidence_final]
all_metrics = []
for n in range(1, len(all_preds) + 1):
ground_truths = copy.deepcopy(ground_truths_copy)
sub_preds = all_preds[0:n]
TP = 0
FP = 0
FN = 0
for pred in sub_preds:
pred_point = pred["point"]
img_size = (pred["size"][1], pred["size"][0])
img_id = pred["image_id"]
dist_threshold = (img_size[0] * 0.01, img_size[1] * 0.01)
gt = [tuple(gt_point) for gt_point in ground_truths[img_id]["points"].tolist()]
gt_copy = copy.deepcopy(gt)
euc_dists = {}
dists = {}
for gt_point in gt_copy:
if gt_point[2] == 0:
dist = (abs(pred_point[0] - gt_point[0]), abs(pred_point[1] - gt_point[1]))
euc_dist = math.sqrt(dist[0] ** 2 + dist[1] ** 2)
euc_dists[gt_point] = euc_dist
dists[gt_point] = dist
euc_dists = sorted(euc_dists.items(), key=lambda x: x[1])
if len(euc_dists) == 0:
FP += 1
continue
nearest_gt_point = euc_dists[0][0]
min_dist = dists[nearest_gt_point]
if min_dist[0] < dist_threshold[0] and min_dist[1] < dist_threshold[1]:
gtip = ground_truths[img_id]["points"]
for i, p in enumerate(gtip):
if (
p[0].item() == nearest_gt_point[0]
and p[1].item() == nearest_gt_point[1]
and p[2].item() == nearest_gt_point[2]
):
# print('qqq', p, nearest_gt_point)
gtip[i, 2] = 1
break
ground_truths[img_id]["points"] = gtip
# print('rrr', ground_truths[img_id]['points'])
TP += 1
continue
FP += 1
for img_id, points in ground_truths.items():
points = points["points"]
for point in points:
if point[2] == 0:
FN += 1
precision = TP / (TP + FP)
recall = TP / (TP + FN)
# print(n, TP, FP, FN, precision, recall)
all_metrics.append((precision, recall))
all_metrics = sorted(all_metrics, key=lambda x: (x[1], x[0]))
p_r_curve_points = {}
for point in all_metrics:
p_r_curve_points[point[1]] = point[0]
p_r_curve_points[0] = 1
p_r_curve_points = sorted(p_r_curve_points.items(), key=lambda d: d[0])
AP = 0
for i, rp in enumerate(p_r_curve_points):
r = rp[0]
p = rp[1]
if i > 0:
small_rectangular_area = (r - p_r_curve_points[i - 1][0]) * p
AP += small_rectangular_area
return AP
def get_results(best_result):
if 1:
preds = best_result[2]
output_points = []
output_edges = []
for triplet in preds:
this_preds = triplet[0]
last_edges = triplet[1]
this_edges = triplet[2]
for this_pred in this_preds:
point = tuple(this_pred["points"].int().tolist())
output_points.append(point)
for last_edge in last_edges:
point1 = tuple(last_edge[0]["points"].int().tolist())
point2 = tuple(last_edge[1]["points"].int().tolist())
edge = (point1, point2)
output_edges.append(edge)
for this_edge in this_edges:
point1 = tuple(this_edge[0]["points"].int().tolist())
point2 = tuple(this_edge[1]["points"].int().tolist())
edge = (point1, point2)
output_edges.append(edge)
return output_points, output_edges
def get_results_visual(best_result):
if 1:
preds = best_result[2]
output_points = []
output_edges = []
for layer_index, triplet in enumerate(preds):
this_preds = triplet[0]
last_edges = triplet[1]
this_edges = triplet[2]
for this_pred in this_preds:
point = tuple(this_pred["points"].int().tolist())
output_points.append([layer_index, point])
for last_edge in last_edges:
point1 = tuple(last_edge[0]["points"].int().tolist())
point2 = tuple(last_edge[1]["points"].int().tolist())
edge = (point1, point2)
output_edges.append([layer_index, edge])
for this_edge in this_edges:
point1 = tuple(this_edge[0]["points"].int().tolist())
point2 = tuple(this_edge[1]["points"].int().tolist())
edge = (point1, point2)
output_edges.append([layer_index, edge])
return output_points, output_edges, len(preds)
def get_results_float_with_semantic(best_result):
preds = best_result[2]
output_points = []
output_edges = []
for triplet in preds:
this_preds = triplet[0]
last_edges = triplet[1]
this_edges = triplet[2]
for this_pred in this_preds:
point = (
this_pred["points"].tolist()[0],
this_pred["points"].tolist()[1],
this_pred["semantic_left_up"].item(),
this_pred["semantic_right_up"].item(),
this_pred["semantic_right_down"].item(),
this_pred["semantic_left_down"].item(),
)
output_points.append(point)
for last_edge in last_edges:
point1 = (
last_edge[0]["points"].tolist()[0],
last_edge[0]["points"].tolist()[1],
last_edge[0]["semantic_left_up"].item(),
last_edge[0]["semantic_right_up"].item(),
last_edge[0]["semantic_right_down"].item(),
last_edge[0]["semantic_left_down"].item(),
)
point2 = (
last_edge[1]["points"].tolist()[0],
last_edge[1]["points"].tolist()[1],
last_edge[1]["semantic_left_up"].item(),
last_edge[1]["semantic_right_up"].item(),
last_edge[1]["semantic_right_down"].item(),
last_edge[1]["semantic_left_down"].item(),
)
edge = (point1, point2)
output_edges.append(edge)
for this_edge in this_edges:
point1 = (
this_edge[0]["points"].tolist()[0],
this_edge[0]["points"].tolist()[1],
this_edge[0]["semantic_left_up"].item(),
this_edge[0]["semantic_right_up"].item(),
this_edge[0]["semantic_right_down"].item(),
this_edge[0]["semantic_left_down"].item(),
)
point2 = (
this_edge[1]["points"].tolist()[0],
this_edge[1]["points"].tolist()[1],
this_edge[1]["semantic_left_up"].item(),
this_edge[1]["semantic_right_up"].item(),
this_edge[1]["semantic_right_down"].item(),
this_edge[1]["semantic_left_down"].item(),
)
edge = (point1, point2)
output_edges.append(edge)
return output_points, output_edges
def calculate_single_sample(
best_result, graph, target_d_rev, target_simple_cycles, target_results, d_rev, simple_cycles, results
):
output_points, output_edges = get_results(best_result)
gt_points = [k for k, v in graph.items()]
gt_edges = []
for k, v in graph.items():
for adj in v:
if adj != (-1, -1):
gt_edge = (k, adj)
if (adj, k) not in gt_edges:
gt_edges.append(gt_edge)
points_TP = 0
points_FP = 0
points_FN = 0
dist_error_x = 0
dist_error_y = 0
dist_error_l2 = 0
gt_points_copy = copy.deepcopy(gt_points)
threshold = 5
for output_point in output_points:
matched = False
for gt_point in gt_points:
if (abs(output_point[0] - gt_point[0]) <= threshold) and (abs(output_point[1] - gt_point[1]) <= threshold):
if gt_point in gt_points_copy:
points_TP += 1
dist_error_x += abs(output_point[0] - gt_point[0])
dist_error_y += abs(output_point[1] - gt_point[1])
dist_error_l2 += (
abs(output_point[0] - gt_point[0]) ** 2 + abs(output_point[1] - gt_point[1]) ** 2
) ** 0.5
matched = True
gt_points_copy.remove(gt_point)
break
if not matched:
points_FP += 1
points_FN = len(gt_points) - points_TP
edges_TP = 0
edges_FP = 0
edges_FN = 0
gt_edges_copy = copy.deepcopy(gt_edges)
threshold = 5
for output_edge in output_edges:
matched = False
for gt_edge in gt_edges:
if (
(
(abs(output_edge[0][0] - gt_edge[0][0]) <= threshold)
and (abs(output_edge[0][1] - gt_edge[0][1]) <= threshold)
)
and (
(abs(output_edge[1][0] - gt_edge[1][0]) <= threshold)
and (abs(output_edge[1][1] - gt_edge[1][1]) <= threshold)
)
) or (
(
(abs(output_edge[0][0] - gt_edge[1][0]) <= threshold)
and (abs(output_edge[0][1] - gt_edge[1][1]) <= threshold)
)
and (
(abs(output_edge[1][0] - gt_edge[0][0]) <= threshold)
and (abs(output_edge[1][1] - gt_edge[0][1]) <= threshold)
)
):
if gt_edge in gt_edges_copy:
edges_TP += 1
matched = True
gt_edges_copy.remove(gt_edge)
break
if not matched:
edges_FP += 1
edges_FN = len(gt_edges) - edges_TP
regions_TP = 0
regions_FP = 0
regions_FN = 0
rooms_TP = 0
rooms_FP = 0
rooms_FN = 0
gt_regions = []
output_regions = []
for target_simple_cycle in target_simple_cycles:
target_polyg = [(point_i[0], point_i[1]) for point_i in target_simple_cycle]
gt_regions.append(target_polyg)
for simple_cycle in simple_cycles:
polyg = [(point_i[0], point_i[1]) for point_i in simple_cycle]
polyg.pop(-1)
output_regions.append(polyg)
gt_regions_copy = copy.deepcopy(gt_regions)
iou_threshold = 0.7
for output_region_i, output_region in enumerate(output_regions):
matched = False
for gt_region_i, gt_region in enumerate(gt_regions):
if poly_iou(Polygon(gt_region), Polygon(output_region)) >= iou_threshold:
if gt_region in gt_regions_copy:
regions_TP += 1
if target_results[gt_region_i] == results[output_region_i]:
rooms_TP += 1
else:
rooms_FP += 1
matched = True
gt_regions_copy.remove(gt_region)
break
if not matched:
regions_FP += 1
rooms_FP += 1
regions_FN = len(gt_regions) - regions_TP
rooms_FN = len(gt_regions) - rooms_TP
# print(regions_TP, regions_FP, regions_FN)
# print(rooms_TP, rooms_FP, rooms_FN)
dist_error = (0, 0, 0)
if points_TP > 0:
dist_error = (dist_error_x, dist_error_y, dist_error_l2)
return (
points_TP,
points_FP,
points_FN,
edges_TP,
edges_FP,
edges_FN,
dist_error,
regions_TP,
regions_FP,
regions_FN,
rooms_TP,
rooms_FP,
rooms_FN,
)
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