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import json
import math
import os
import shutil
from code.base import BaseEvaluation, DetectionEvalConfig, EvalResult
from typing import List
import cv2
import numpy as np
from hat.evaluation.detection3d import evaluate
from hat.visualize.detection3d.draw_samples import (
draw_sample,
list_failure_samples,
)
from aidisdk.experiment import Image, Line, Table
def generate_plot(file_name: str) -> List[dict]:
plots = []
results = json.load(open(file_name, "rb"))
recall = results["recall"]
precision = results["precision"]
fppi = results["fppi"]
tab1_data = []
for idx, _item in enumerate(recall):
data_dict = {"recall": recall[idx], "precision": precision[idx]}
tab1_data.append(data_dict)
tab2_data = []
for idx, _item in enumerate(fppi):
data_dict = {"fppi": fppi[idx], "recall": recall[idx]}
tab2_data.append(data_dict)
table1 = Table(
name="recall_vs_precision-{}".format(
file_name.split("/")[-1].split(".")[0]
),
columns=["recall", "precision"],
data=tab1_data,
)
table2 = Table(
name="fppi_vs_recall-{}".format(
file_name.split("/")[-1].split(".")[0]
),
columns=["fppi", "recall"],
data=tab2_data,
)
plot1 = {
"Table": table1,
"Line": Line(x="recall", y="precision", stroke="recall-precision"),
}
plot2 = {
"Table": table2,
"Line": Line(x="fppi", y="recall", stroke="fppi-recall"),
}
plots.append(plot1)
plots.append(plot2)
return plots
class Detection3dEval(BaseEvaluation):
def __init__(self, run_config):
super().__init__(run_config)
def preprocess(self) -> DetectionEvalConfig:
return super().detection_preprocess()
def evaluate(self, eval_config: DetectionEvalConfig) -> EvalResult:
if os.path.exists("outputs"):
shutil.rmtree("outputs")
os.makedirs("outputs", exist_ok=True)
results = evaluate(
eval_config.gt,
eval_config.prediction,
eval_config.setting,
"outputs",
)
summary = {}
for result in results:
for key, item in result.items():
val = item
summary[key] = val
tables = []
results = json.load(open("outputs/tables.json", "rb"))
for result in results:
data = []
for dict_data in result["data"]:
new_dict_data = {}
for k, v in dict_data.items():
if type(v) == float and math.isnan(v):
v = "nan"
if type(v) in [list, tuple, set]:
v = str(v)
new_dict_data[k] = v
data.append(new_dict_data)
table = Table(
name=result["name"],
columns=result["header"],
data=data,
)
tables.append(table)
plots = []
if os.path.exists("outputs/result.json"):
plots_1 = generate_plot("outputs/result.json")
plots.extend(plots_1)
if os.path.exists("outputs/result_auto.json"):
plots_2 = generate_plot("outputs/result_auto.json")
plots.extend(plots_2)
images = []
samples = list_failure_samples(open("outputs/all.json", "rb"))
if os.path.exists("outputs/samples"):
shutil.rmtree("outputs/samples")
os.makedirs("outputs/samples", exist_ok=True)
for sample in samples:
fp_score = max(
[
det["score"]
for det in list(
filter(
lambda det: det["eval_type"] == "FP"
or det["eval_type"] == "ignore",
sample["det_bboxes"],
)
)
]
+ [-1]
)
tp_score = max(
[
det["score"]
for det in list(
filter(
lambda det: det["eval_type"] == "TP",
sample["det_bboxes"],
)
)
]
+ [-1]
)
tp_drot = max(
[
det["metrics"]["drot"]
for det in list(
filter(
lambda det: det["eval_type"] == "TP",
sample["det_bboxes"],
)
)
]
+ [-1]
)
tp_dxy = max(
[
det["metrics"]["dxy"]
for det in list(
filter(
lambda det: det["eval_type"] == "TP",
sample["det_bboxes"],
)
)
]
+ [-1]
)
image_name = sample["image_key"]
image_file_path = os.path.join(eval_config.images_dir, image_name)
output_file_path = os.path.join("outputs/samples/", image_name)
if os.path.exists(image_file_path):
with open(image_file_path, "rb") as image_file:
image_content = image_file.read()
npar = np.fromstring(image_content, dtype="uint8")
image = cv2.imdecode(npar, 1)
image = draw_sample(image, sample)
cv2.imwrite(output_file_path, image)
image = Image(
image_name,
attrs={
"fp_score": fp_score,
"tp_score": tp_score,
"tp_drot": tp_drot,
"tp_dxy": tp_dxy,
},
)
image.add_slice(data_or_path=output_file_path)
images.append(image)
eval_result = EvalResult(
summary=summary,
tables=tables,
plots=plots,
images=images,
)
return eval_result
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