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inference.py
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inference.py
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# -*- coding: utf-8 -*-
import os
import cv2
import argparse
import glob as glob
import numpy as np
from utilities import blur_img_bboxes
def two_stage_lp(img_paths, settings_car, settings_lp):
from imageai.Detection.Custom import CustomObjectDetection
detector_car = CustomObjectDetection()
detector_car.setModelTypeAsYOLOv3()
detector_car.setModelPath(settings_car["model_path"])
detector_car.setJsonPath(settings_car["config_path"])
detector_car.loadModel()
detector_lp = CustomObjectDetection()
detector_lp.setModelTypeAsYOLOv3()
detector_lp.setModelPath(settings_lp["model_path"])
detector_lp.setJsonPath(settings_lp["config_path"])
detector_lp.loadModel()
crop_image = lambda img, bb: img[bb[1]:bb[3] + 1, bb[0]:bb[2] + 1]
detections_image = []
for in_img_path in img_paths:
detections = []
img = cv2.imread(in_img_path)
tmp = detector_car.detectObjectsFromImage(input_image=in_img_path,
output_type="array",
minimum_percentage_probability=\
settings_car["threshold"])[1]
rois = [(crop_image(img, det["box_points"]), det["box_points"]) \
for det in tmp if det["name"] in ("car","truck","motorbike","bus")]
for i, roi in enumerate(rois):
tmp = detector_lp.detectObjectsFromImage(input_image=roi[0],
input_type="array",
output_type="array",
minimum_percentage_probability=\
settings_lp["threshold"])[1]
for det_lp in tmp:
det_lp["box_points"] = (np.asarray(det_lp["box_points"]) +\
np.tile(roi[1][:2], 2)).tolist()
detections += tmp
detections_image.append(detections)
return detections_image
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Infer two models in parallel')
parser.add_argument('--img_dir',
dest='img_dir',
type=str,
required=True,
help="path to images directory"
)
parser.add_argument('--config',
nargs=2,
dest='cfg_list',
required=True,
help="a list of paths to json configuration files (1.vehicle detection, 2.lp detection)"
)
parser.add_argument('--model',
nargs=2,
dest='model_list',
required=True,
help="a list of paths to the model files (1.vehicle detection, 2.lp detection)"
)
parser.add_argument('--out_dir',
dest='out_dir',
type=str,
required=False,
default=os.getcwd(),
help="path to the output directory"
)
parser.add_argument('--vehicle_thresh',
dest='vehicle_thresh',
type=float,
required=False,
default=0.3,
help="vehicle detection threshold"
)
parser.add_argument('--lp_thresh',
dest='lp_thresh',
type=float,
required=False,
default=0.5,
help="lp detection threshold"
)
args = parser.parse_args()
img_dir = args.img_dir
out_dir = args.out_dir
model_list = args.model_list
cfg_list = args.cfg_list
vehicle_thresh = 100*args.vehicle_thresh
lp_thresh = 100*args.lp_thresh
for i in range(len(model_list)):
assert os.path.isfile(model_list[i]), "Not a valid model file %s"\
% (model_list[i])
assert os.path.isfile(cfg_list[i]), "Not a valid model file %s"\
% (cfg_list[i])
for i in range(len(model_list)):
assert os.path.isfile(model_list[i]), "Not a valid model file %s"\
% (model_list[i])
assert os.path.isfile(cfg_list[i]), "Not a valid model file %s"\
% (cfg_list[i])
settings_orig = {"model_path": os.path.abspath(args.model_list[0]),
"config_path": os.path.abspath(args.cfg_list[0]),
"threshold": vehicle_thresh}
settings_cus = {"model_path": os.path.abspath(args.model_list[1]),
"config_path": os.path.abspath(args.cfg_list[1]),
"threshold": lp_thresh}
input_paths = glob.glob(os.path.join(img_dir, "*.png"))
if not os.path.isdir(out_dir):
os.mkdir(out_dir)
detections_lp = two_stage_lp(input_paths, settings_orig, settings_cus)
for i in range(len(input_paths)):
blur_img_bboxes(input_paths[i], detections_lp[i], out_dir)