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load_api_results.py
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load_api_results.py
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"""
load_api_results.py
DEPRECATED
As of 2023.12, this module is used in postprocessing and RDE. Not recommended
for new code.
Loads the output of the batch processing API (json) into a Pandas dataframe.
Includes functions to read/write the (very very old) .csv results format.
"""
#%% Imports
import json
import os
from typing import Dict, Mapping, Optional, Tuple
import pandas as pd
from megadetector.utils import ct_utils
#%% Functions for loading .json results into a Pandas DataFrame, and writing back to .json
def load_api_results(api_output_path: str, normalize_paths: bool = True,
filename_replacements: Optional[Mapping[str, str]] = None,
force_forward_slashes: bool = True
) -> Tuple[pd.DataFrame, Dict]:
r"""
Loads json-formatted MegaDetector results to a Pandas DataFrame.
Args:
api_output_path: path to the output json file
normalize_paths: whether to apply os.path.normpath to the 'file' field
in each image entry in the output file
filename_replacements: replace some path tokens to match local paths to
the original blob structure
force_forward_slashes: whether to convert backslashes to forward slashes
in filenames
Returns:
detection_results: pd.DataFrame, contains at least the columns ['file', 'detections','failure']
other_fields: a dict containing fields in the results other than 'images'
"""
print('Loading results from {}'.format(api_output_path))
with open(api_output_path) as f:
detection_results = json.load(f)
# Validate that this is really a detector output file
for s in ['info', 'detection_categories', 'images']:
assert s in detection_results, 'Missing field {} in detection results'.format(s)
# Fields in the output json other than 'images'
other_fields = {}
for k, v in detection_results.items():
if k != 'images':
other_fields[k] = v
if normalize_paths:
for image in detection_results['images']:
image['file'] = os.path.normpath(image['file'])
if force_forward_slashes:
for image in detection_results['images']:
image['file'] = image['file'].replace('\\','/')
# Replace some path tokens to match local paths to original blob structure
if filename_replacements is not None:
for string_to_replace in filename_replacements.keys():
replacement_string = filename_replacements[string_to_replace]
for im in detection_results['images']:
im['file'] = im['file'].replace(string_to_replace,replacement_string)
print('Converting results to dataframe')
# If this is a newer file that doesn't include maximum detection confidence values,
# add them, because our unofficial internal dataframe format includes this.
for im in detection_results['images']:
if 'max_detection_conf' not in im:
im['max_detection_conf'] = ct_utils.get_max_conf(im)
# Pack the json output into a Pandas DataFrame
detection_results = pd.DataFrame(detection_results['images'])
print('Finished loading MegaDetector results for {} images from {}'.format(
len(detection_results),api_output_path))
return detection_results, other_fields
def write_api_results(detection_results_table, other_fields, out_path):
"""
Writes a Pandas DataFrame to the MegaDetector .json format.
"""
print('Writing detection results to {}'.format(out_path))
fields = other_fields
images = detection_results_table.to_json(orient='records',
double_precision=3)
images = json.loads(images)
fields['images'] = images
# Convert the 'version' field back to a string as per format convention
try:
version = other_fields['info']['format_version']
if not isinstance(version,str):
other_fields['info']['format_version'] = str(version)
except Exception:
print('Warning: error determining format version')
pass
# Remove 'max_detection_conf' as per newer file convention (format >= v1.3)
try:
version = other_fields['info']['format_version']
version = float(version)
if version >= 1.3:
for im in images:
if 'max_detection_conf' in im:
del im['max_detection_conf']
except Exception:
print('Warning: error removing max_detection_conf from output')
pass
with open(out_path, 'w') as f:
json.dump(fields, f, indent=1)
print('Finished writing detection results to {}'.format(out_path))
def load_api_results_csv(filename, normalize_paths=True, filename_replacements={}, nrows=None):
"""
[DEPRECATED]
Loads .csv-formatted MegaDetector results to a pandas table
"""
print('Loading MegaDetector results from {}'.format(filename))
detection_results = pd.read_csv(filename,nrows=nrows)
print('De-serializing MegaDetector results from {}'.format(filename))
# Confirm that this is really a detector output file
for s in ['image_path','max_confidence','detections']:
assert s in detection_results.columns
# Normalize paths to simplify comparisons later
if normalize_paths:
detection_results['image_path'] = detection_results['image_path'].apply(os.path.normpath)
# De-serialize detections
detection_results['detections'] = detection_results['detections'].apply(json.loads)
# Optionally replace some path tokens to match local paths to the original blob structure
# string_to_replace = list(options.detector_output_filename_replacements.keys())[0]
for string_to_replace in filename_replacements:
replacement_string = filename_replacements[string_to_replace]
# iRow = 0
for iRow in range(0,len(detection_results)):
row = detection_results.iloc[iRow]
fn = row['image_path']
fn = fn.replace(string_to_replace,replacement_string)
detection_results.at[iRow,'image_path'] = fn
print('Finished loading and de-serializing MD results for {} images from {}'.format(
len(detection_results),filename))
return detection_results
def write_api_results_csv(detection_results, filename):
"""
[DEPRECATED]
Writes a Pandas table to csv in a way that's compatible with the .csv output
format. Currently just a wrapper around to_csv that forces output writing
to go through a common code path.
"""
print('Writing detection results to {}'.format(filename))
detection_results.to_csv(filename, index=False)
print('Finished writing detection results to {}'.format(filename))