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reid_rotation_test.py
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reid_rotation_test.py
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from contextlib import contextmanager
import cv2 as cv
from docopt import docopt
from matplotlib import pyplot as plt
import numpy as np
import os
from scipy.spatial import distance
import sys
import torch
from models.deepsort_utils.fastreid_adaptor import FastReID
from models.apparence_bbox_detector import ApparenceBBoxDetector
np.random.seed(0)
WIDTH = 64
HEIGHT = 64 # 128
YLIM_MAX = 1
@contextmanager
def VideoCapture(input_video):
# findFileOrKeep allows more searching paths
capture = cv.VideoCapture(cv.samples.findFileOrKeep(input_video))
if not capture.isOpened():
print('Unable to open: ' + input_video, file=sys.stderr)
exit(0)
try:
yield capture
finally:
# Release the video capture object at the end
capture.release()
def crop_pad(frame, bbox, pad_color, crop_h, crop_w):
h = int(bbox[3])
w = int(bbox[2])
crop = frame[int(bbox[1]) : int(bbox[1]) + h, int(bbox[0]) : int(bbox[0]) + w, :]
if h > crop_h:
excess = h - crop_h
crop = crop[excess // 2 : excess // 2 + crop_h, :, :]
h = crop_h
if w > crop_w:
excess = w - crop_w
crop = crop[:, excess // 2 : excess // 2 + crop_w, :]
w = crop_w
if h < crop_h or w < crop_w:
pad_h = (crop_h - h) // 2
pad_w = (crop_w - w) // 2
pad = ((pad_h, crop_h - h - pad_h), (pad_w, crop_w - w - pad_w))
crop = np.stack([np.pad(crop[:, :, c], pad, mode='constant', constant_values=pad_color[c]) for c in range(3)], axis=2)
crop = np.moveaxis(crop, [0, 1, 2], [1, 2, 0])
return crop
def pad_reshape(frame, bbox, pad_color, crop_h, crop_w):
h = int(bbox[3])
w = int(bbox[2])
crop = frame[int(bbox[1]) : int(bbox[1]) + h, int(bbox[0]) : int(bbox[0]) + w, :]
ar = crop_h / crop_w
pad_h = int((w * ar - h) // 2)
pad_w = int((h / ar - w) // 2)
pad = ((pad_h, int(w * ar - h - pad_h)), (0, 0)) if h < w * ar else ((0, 0), (pad_w, int(h / ar - w - pad_w)))
crop = np.stack([np.pad(crop[:, :, c], pad, mode='constant', constant_values=pad_color[c]) for c in range(3)], axis=2)
crop = cv.resize(crop, (crop_w, crop_h), interpolation=cv.INTER_AREA)
crop = np.moveaxis(crop, [0, 1, 2], [1, 2, 0])
return crop
def rotate(image, angle, center=None, scale=1.0):
(h, w) = image.shape[:2]
if center is None:
center = (w / 2, h / 2)
# Perform the rotation
M = cv.getRotationMatrix2D(center, angle, scale)
rotated = cv.warpAffine(image, M, (w, h))
return rotated, M
def crop_pad_rotations(frame, bbox, background_color, height, width, axis):
output = []
for rot in axis:
h = int(min(bbox[3], height))
w = int(min(bbox[2], width))
center = np.array((bbox[0] + w/2, bbox[1] + h/2))
rot_frame, M = rotate(frame, rot, center.tolist())
#origin = np.dot(bbox[:2] - center, M).astype(int)[:2] + center
deltas = bbox[2:4] * np.abs(np.cos(np.deg2rad(rot))) + bbox[4:2:-1] * np.abs(np.sin(np.deg2rad(rot)))
deltas = deltas.astype(int)
h = int(min(deltas[1], height))
w = int(min(deltas[0], width))
bbox = np.array([int(center[0] - w / 2), int(center[1] - h / 2), w, h])
crop = crop_pad(rot_frame, bbox, background_color, height, width)
output.append(crop)
return output
def pad_reshape_rotations(frame, bbox, background_color, height, width, axis):
output = []
for rot in axis:
h = int(min(bbox[3], height))
w = int(min(bbox[2], width))
center = np.array((bbox[0] + w/2, bbox[1] + h/2))
rot_frame, M = rotate(frame, rot, center.tolist())
#origin = np.dot(bbox[:2] - center, M).astype(int)[:2] + center
deltas = bbox[2:4] * np.abs(np.cos(np.deg2rad(rot))) + bbox[4:2:-1] * np.abs(np.sin(np.deg2rad(rot)))
deltas = deltas.astype(int)
h = int(min(deltas[1], height))
w = int(min(deltas[0], width))
bbox = np.array([int(center[0] - w / 2), int(center[1] - h / 2), w, h])
crop = pad_reshape(rot_frame, bbox, background_color, height, width)
output.append(crop)
return output
def ant_with_itself(input_video, seq_dets, apparence_model_applier, frame_ids, axis):
seq_dets_iter = iter(seq_dets)
distances = []
with VideoCapture(input_video) as capture:
# We shold be able to skip loading empty frames
for frame_id in frame_ids:
capture.set(cv.CAP_PROP_POS_FRAMES, frame_id - 1)
_, frame = capture.read()
if frame is None:
print (f'Frame {frame_id} is None', file=sys.stderr)
break
bbox = next(seq_dets_iter)
background_color = np.mean(frame, (0, 1))
# inputs = torch.Tensor(np.stack(crop_pad_rotations(frame, bbox[2:], background_color, HEIGHT, WIDTH, axis), axis=0))
inputs = torch.Tensor(np.stack(pad_reshape_rotations(frame, bbox[2:], background_color, HEIGHT, WIDTH, axis), axis=0))
feats = apparence_model_applier(inputs)
base = feats[0]
distances.append([distance.cosine(feat, base) for feat in feats])
distances = np.array(distances)
min_dist = distances.min(axis=0)
max_dist = distances.max(axis=0)
mean_dist = distances.mean(axis=0)
return min_dist, max_dist, mean_dist
def ant_with_another(input_video, seq_dets1, seq_dets2, apparence_model_applier, frame_ids1, frame_ids2, axis):
seq_dets_iter1 = iter(seq_dets1)
seq_dets_iter2 = iter(seq_dets2)
distances = []
with VideoCapture(input_video) as capture:
# We shold be able to skip loading empty frames
for frame_id1, frame_id2 in zip(frame_ids1, frame_ids2):
capture.set(cv.CAP_PROP_POS_FRAMES, frame_id1 - 1)
_, frame1 = capture.read()
if frame1 is None:
print (f'Frame {frame_id1} is None', file=sys.stderr)
break
capture.set(cv.CAP_PROP_POS_FRAMES, frame_id2 - 1)
_, frame2 = capture.read()
if frame2 is None:
print (f'Frame {frame_id2} is None', file=sys.stderr)
break
bbox1 = next(seq_dets_iter1)
bbox2 = next(seq_dets_iter2)
background_color1 = np.mean(frame1, (0, 1))
background_color2 = np.mean(frame2, (0, 1))
# base_input = torch.Tensor(crop_pad(frame2, bbox2[2:], background_color2, HEIGHT, WIDTH)[np.newaxis, :])
# rotation_inputs = torch.Tensor(np.stack(crop_pad_rotations(frame1, bbox1[2:], background_color1, HEIGHT, WIDTH, axis), axis=0))
base_input = torch.Tensor(pad_reshape(frame2, bbox2[2:], background_color2, HEIGHT, WIDTH)[np.newaxis, :])
rotation_inputs = torch.Tensor(np.stack(pad_reshape_rotations(frame1, bbox1[2:], background_color1, HEIGHT, WIDTH, axis), axis=0))
base = apparence_model_applier(base_input)[0]
rotation_feats = apparence_model_applier(rotation_inputs)
distances.append([distance.cosine(feat, base) for feat in rotation_feats])
distances = np.array(distances)
min_dist = distances.min(axis=0)
max_dist = distances.max(axis=0)
mean_dist = distances.mean(axis=0)
return min_dist, max_dist, mean_dist
CONFIG_FILE = 'runs/apparence/ants_fastreid_vocal-sweep-6_cee5t8ta/config.yaml'
WEIGHTS_PATH = 'runs/apparence/ants_fastreid_vocal-sweep-6_cee5t8ta/model_best.pth'
DOCTEXT = f"""
Usage:
reid_rotation_test.py <input_video> <trckFile> <output_file> [--config=<cf>] [--weights=<wp>] [--num_imgs=<ni>] [--num_steps=<ns>] [--max_ids=<mi>]
reid_rotation_test.py -h | --help
Options:
--config=<cf> Config file from the fastreid model. [default: {CONFIG_FILE}]
--weights=<wp> Weights path from the fastreid model. [default: {WEIGHTS_PATH}]
--num_imgs=<ni> Number of ants detections used for the rotation experiments [default: 5]
--num_steps=<ns> How many divisions of 360º are used when rotating the images [default: 18]
--max_ids=<mi> Number of ids used for rotation, it cannot be greater or equal to the total number of ids (there must be available ids for query) [default: 5]
"""
if __name__ == '__main__':
args = docopt(DOCTEXT, argv=sys.argv[1:], help=True, version=None, options_first=False)
input_video = args['<input_video>']
trckFile = args['<trckFile>']
output_file = args['<output_file>']
config_file = args['--config']
weights_path = args['--weights']
num_imgs = int(args['--num_imgs'])
num_steps = int(args['--num_steps'])
max_ids = int(args['--max_ids'])
apparence_model = FastReID(config_file, weights_path)
def apparence_model_applier(x):
with torch.no_grad():
out = apparence_model(x)
out = torch.nn.functional.normalize(out, dim=-1).numpy(force=True)
return out
seq_dets = np.loadtxt(trckFile, delimiter=',')
all_ids = np.unique(seq_dets[:, 1])
rot_ids = all_ids[:max_ids]
query_ids = all_ids[~np.isin(all_ids, rot_ids)]
seq_dets1 = seq_dets[np.isin(seq_dets[:, 1], rot_ids)]
subset1 = np.arange(len(seq_dets1))
np.random.shuffle(subset1)
subset1 = subset1[:num_imgs]
seq_dets1 = seq_dets1[subset1]
frame_ids1 = seq_dets1[:, 0].copy()
seq_dets2 = seq_dets[np.isin(seq_dets[:, 1], query_ids)]
subset2 = np.arange(len(seq_dets2))
np.random.shuffle(subset2)
subset2 = subset2[:num_imgs]
seq_dets2 = seq_dets2[subset2]
frame_ids2 = seq_dets2[:, 0].copy()
axis = np.linspace(0, 360, num_steps, endpoint=False)
min_dist, max_dist, mean_dist = ant_with_itself(input_video, seq_dets1, apparence_model_applier, frame_ids1, axis)
rad_axis = np.deg2rad(axis)
fig, (ax1, ax2) = plt.subplots(1, 2, subplot_kw={'projection': 'polar'}, figsize=(6.4 * 2.5, 4.8 * 1.25* 1.2), constrained_layout=True)
fig.suptitle('Apparence Features Rotation Test\n(The radial axis contains the cosine distance, the angular axis contains the rotation in degrees)')
ax1.plot(rad_axis, mean_dist)
ax1.plot(rad_axis, min_dist)
ax1.plot(rad_axis, max_dist)
ax1.set_ylim([0, YLIM_MAX])
ax1.grid(True)
ax1.legend(['mean', 'min', 'max'])
ax1.set_title(f'Ant crop compared with itself rotated\n(experiment done for {num_imgs} detections)')
min_dist, max_dist, mean_dist = ant_with_another(input_video, seq_dets1, seq_dets2, apparence_model_applier, frame_ids1, frame_ids2, axis)
ax2.plot(rad_axis, mean_dist)
ax2.plot(rad_axis, min_dist)
ax2.plot(rad_axis, max_dist)
ax2.set_ylim([0, YLIM_MAX])
ax2.grid(True)
ax2.legend(['mean', 'min', 'max'])
ax2.set_title(f'Ant crop compared with another ant rotated\n(experiment done for {num_imgs} pairs of different identities)')
fig.savefig(output_file, dpi=300)