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generate_detections.py
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generate_detections.py
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# vim: expandtab:ts=4:sw=4
import os
import errno
import argparse
import numpy as np
import cv2
import tensorrt as trt
import common
def extract_image_patch(image, bbox, patch_shape):
"""Extract image patch from bounding box.
Parameters
----------
image : ndarray
The full image.
bbox : array_like
The bounding box in format (x, y, width, height).
patch_shape : Optional[array_like]
This parameter can be used to enforce a desired patch shape
(height, width). First, the `bbox` is adapted to the aspect ratio
of the patch shape, then it is clipped at the image boundaries.
If None, the shape is computed from :arg:`bbox`.
Returns
-------
ndarray | NoneType
An image patch showing the :arg:`bbox`, optionally reshaped to
:arg:`patch_shape`.
Returns None if the bounding box is empty or fully outside of the image
boundaries.
"""
bbox = np.array(bbox)
if patch_shape is not None:
# correct aspect ratio to patch shape
target_aspect = float(patch_shape[1]) / patch_shape[0]
new_width = target_aspect * bbox[3]
bbox[0] -= (new_width - bbox[2]) / 2
bbox[2] = new_width
# convert to top left, bottom right
bbox[2:] += bbox[:2]
bbox = bbox.astype(np.int)
# clip at image boundaries
bbox[:2] = np.maximum(0, bbox[:2])
bbox[2:] = np.minimum(np.asarray(image.shape[:2][::-1]) - 1, bbox[2:])
if np.any(bbox[:2] >= bbox[2:]):
return None
sx, sy, ex, ey = bbox
image = image[sy:ey, sx:ex]
image = cv2.resize(image, tuple(patch_shape[::-1]))
return image
class ImageEncoder(object):
def __init__(self, engine_filename, logger):
self.engine = common.deserialize_engine_from_file(engine_path=engine_filename, logger=logger)
inputs, outputs, bindings, stream = common.allocate_buffers(self.engine)
self.inputs = inputs
self.outputs = outputs
self.bindings = bindings
self.stream = stream
# Create execution context from engine
self.context = self.engine.create_execution_context()
self.feature_dim = 128
self.image_shape = (128, 64, 3)
def __call__(self, patches, batch_size=32):
np.copyto(self.inputs[0].host, patches.ravel())
print(self.inputs[0].host)
# # Do inferences
[out]= common.do_inference(self.context, bindings=self.bindings, inputs=self.inputs, outputs=self.outputs, stream=self.stream, batch_size=4)
return out
def create_box_encoder(model_filename, logger, batch_size=32):
image_encoder = ImageEncoder(model_filename, logger)
image_shape = image_encoder.image_shape
def encoder(image, boxes):
image_patches = []
for box in boxes:
patch = extract_image_patch(image, box, image_shape[:2])
patch = np.transpose(patch, (2,0,1))
image_patches.append(patch)
image_patches = np.asarray(image_patches)
print(image_patches.shape)
return image_encoder(image_patches, batch_size)
return encoder
def generate_detections(encoder, mot_dir, output_dir, detection_dir=None):
"""Generate detections with features.
Parameters
----------
encoder : Callable[image, ndarray] -> ndarray
The encoder function takes as input a BGR color image and a matrix of
bounding boxes in format `(x, y, w, h)` and returns a matrix of
corresponding feature vectors.
mot_dir : str
Path to the MOTChallenge directory (can be either train or test).
output_dir
Path to the output directory. Will be created if it does not exist.
detection_dir
Path to custom detections. The directory structure should be the default
MOTChallenge structure: `[sequence]/det/det.txt`. If None, uses the
standard MOTChallenge detections.
"""
if detection_dir is None:
detection_dir = mot_dir
try:
os.makedirs(output_dir)
except OSError as exception:
if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
pass
else:
raise ValueError(
"Failed to created output directory '%s'" % output_dir)
for sequence in os.listdir(mot_dir):
print("Processing %s" % sequence)
sequence_dir = os.path.join(mot_dir, sequence)
image_dir = os.path.join(sequence_dir, "img1")
image_filenames = {
int(os.path.splitext(f)[0]): os.path.join(image_dir, f)
for f in os.listdir(image_dir)}
detection_file = os.path.join(
detection_dir, sequence, "det/det.txt")
detections_in = np.loadtxt(detection_file, delimiter=',')
detections_out = []
frame_indices = detections_in[:, 0].astype(np.int)
min_frame_idx = frame_indices.astype(np.int).min()
max_frame_idx = frame_indices.astype(np.int).max()
for frame_idx in range(min_frame_idx, max_frame_idx + 1):
print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
mask = frame_indices == frame_idx
rows = detections_in[mask]
if frame_idx not in image_filenames:
print("WARNING could not find image for frame %d" % frame_idx)
continue
bgr_image = cv2.imread(
image_filenames[frame_idx], cv2.IMREAD_COLOR)
features = encoder(bgr_image, rows[:, 2:6].copy())
detections_out += [np.r_[(row, feature)] for row, feature
in zip(rows, features)]
output_filename = os.path.join(output_dir, "%s.npy" % sequence)
np.save(
output_filename, np.asarray(detections_out), allow_pickle=False)
def parse_args():
"""Parse command line arguments.
"""
parser = argparse.ArgumentParser(description="Re-ID feature extractor")
parser.add_argument(
"--model",
default="resources/networks/mars-small128.pb",
help="Path to freezed inference graph protobuf.")
parser.add_argument(
"--mot_dir", help="Path to MOTChallenge directory (train or test)",
required=True)
parser.add_argument(
"--detection_dir", help="Path to custom detections. Defaults to "
"standard MOT detections Directory structure should be the default "
"MOTChallenge structure: [sequence]/det/det.txt", default=None)
parser.add_argument(
"--output_dir", help="Output directory. Will be created if it does not"
" exist.", default="detections")
return parser.parse_args()
def main():
args = parse_args()
encoder = create_box_encoder(args.model, batch_size=32)
generate_detections(encoder, args.mot_dir, args.output_dir,
args.detection_dir)
if __name__ == "__main__":
main()