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# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is based on https://github.com/WWangYuHsiang/SMILEtrack/blob/main/BoT-SORT/tracker/gmc.py
"""
import cv2
import matplotlib.pyplot as plt
import numpy as np
import copy
import time
class GMC:
def __init__(self, method='sparseOptFlow', downscale=2, verbose=None):
super(GMC, self).__init__()
self.method = method
self.downscale = max(1, int(downscale))
if self.method == 'orb':
self.detector = cv2.FastFeatureDetector_create(20)
self.extractor = cv2.ORB_create()
self.matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
elif self.method == 'sift':
self.detector = cv2.SIFT_create(
nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
self.extractor = cv2.SIFT_create(
nOctaveLayers=3, contrastThreshold=0.02, edgeThreshold=20)
self.matcher = cv2.BFMatcher(cv2.NORM_L2)
elif self.method == 'ecc':
number_of_iterations = 5000
termination_eps = 1e-6
self.warp_mode = cv2.MOTION_EUCLIDEAN
self.criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
number_of_iterations, termination_eps)
elif self.method == 'sparseOptFlow':
self.feature_params = dict(
maxCorners=1000,
qualityLevel=0.01,
minDistance=1,
blockSize=3,
useHarrisDetector=False,
k=0.04)
# self.gmc_file = open('GMC_results.txt', 'w')
elif self.method == 'file' or self.method == 'files':
seqName = verbose[0]
ablation = verbose[1]
if ablation:
filePath = r'tracker/GMC_files/MOT17_ablation'
else:
filePath = r'tracker/GMC_files/MOTChallenge'
if '-FRCNN' in seqName:
seqName = seqName[:-6]
elif '-DPM' in seqName:
seqName = seqName[:-4]
elif '-SDP' in seqName:
seqName = seqName[:-4]
self.gmcFile = open(filePath + "/GMC-" + seqName + ".txt", 'r')
if self.gmcFile is None:
raise ValueError("Error: Unable to open GMC file in directory:"
+ filePath)
elif self.method == 'none' or self.method == 'None':
self.method = 'none'
else:
raise ValueError("Error: Unknown CMC method:" + method)
self.prevFrame = None
self.prevKeyPoints = None
self.prevDescriptors = None
self.initializedFirstFrame = False
def apply(self, raw_frame, detections=None):
if self.method == 'orb' or self.method == 'sift':
return self.applyFeaures(raw_frame, detections)
elif self.method == 'ecc':
return self.applyEcc(raw_frame, detections)
elif self.method == 'sparseOptFlow':
return self.applySparseOptFlow(raw_frame, detections)
elif self.method == 'file':
return self.applyFile(raw_frame, detections)
elif self.method == 'none':
return np.eye(2, 3)
else:
return np.eye(2, 3)
def applyEcc(self, raw_frame, detections=None):
# Initialize
height, width, _ = raw_frame.shape
frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
H = np.eye(2, 3, dtype=np.float32)
# Downscale image (TODO: consider using pyramids)
if self.downscale > 1.0:
frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
frame = cv2.resize(frame, (width // self.downscale,
height // self.downscale))
width = width // self.downscale
height = height // self.downscale
# Handle first frame
if not self.initializedFirstFrame:
# Initialize data
self.prevFrame = frame.copy()
# Initialization done
self.initializedFirstFrame = True
return H
# Run the ECC algorithm. The results are stored in warp_matrix.
# (cc, H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode, self.criteria)
try:
(cc,
H) = cv2.findTransformECC(self.prevFrame, frame, H, self.warp_mode,
self.criteria, None, 1)
except:
print('Warning: find transform failed. Set warp as identity')
return H
def applyFeaures(self, raw_frame, detections=None):
# Initialize
height, width, _ = raw_frame.shape
frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
H = np.eye(2, 3)
# Downscale image (TODO: consider using pyramids)
if self.downscale > 1.0:
# frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
frame = cv2.resize(frame, (width // self.downscale,
height // self.downscale))
width = width // self.downscale
height = height // self.downscale
# find the keypoints
mask = np.zeros_like(frame)
# mask[int(0.05 * height): int(0.95 * height), int(0.05 * width): int(0.95 * width)] = 255
mask[int(0.02 * height):int(0.98 * height), int(0.02 * width):int(
0.98 * width)] = 255
if detections is not None:
for det in detections:
tlbr = (det[:4] / self.downscale).astype(np.int_)
mask[tlbr[1]:tlbr[3], tlbr[0]:tlbr[2]] = 0
keypoints = self.detector.detect(frame, mask)
# compute the descriptors
keypoints, descriptors = self.extractor.compute(frame, keypoints)
# Handle first frame
if not self.initializedFirstFrame:
# Initialize data
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
self.prevDescriptors = copy.copy(descriptors)
# Initialization done
self.initializedFirstFrame = True
return H
# Match descriptors.
knnMatches = self.matcher.knnMatch(self.prevDescriptors, descriptors, 2)
# Filtered matches based on smallest spatial distance
matches = []
spatialDistances = []
maxSpatialDistance = 0.25 * np.array([width, height])
# Handle empty matches case
if len(knnMatches) == 0:
# Store to next iteration
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
self.prevDescriptors = copy.copy(descriptors)
return H
for m, n in knnMatches:
if m.distance < 0.9 * n.distance:
prevKeyPointLocation = self.prevKeyPoints[m.queryIdx].pt
currKeyPointLocation = keypoints[m.trainIdx].pt
spatialDistance = (
prevKeyPointLocation[0] - currKeyPointLocation[0],
prevKeyPointLocation[1] - currKeyPointLocation[1])
if (np.abs(spatialDistance[0]) < maxSpatialDistance[0]) and \
(np.abs(spatialDistance[1]) < maxSpatialDistance[1]):
spatialDistances.append(spatialDistance)
matches.append(m)
meanSpatialDistances = np.mean(spatialDistances, 0)
stdSpatialDistances = np.std(spatialDistances, 0)
inliesrs = (spatialDistances - meanSpatialDistances
) < 2.5 * stdSpatialDistances
goodMatches = []
prevPoints = []
currPoints = []
for i in range(len(matches)):
if inliesrs[i, 0] and inliesrs[i, 1]:
goodMatches.append(matches[i])
prevPoints.append(self.prevKeyPoints[matches[i].queryIdx].pt)
currPoints.append(keypoints[matches[i].trainIdx].pt)
prevPoints = np.array(prevPoints)
currPoints = np.array(currPoints)
# Draw the keypoint matches on the output image
if 0:
matches_img = np.hstack((self.prevFrame, frame))
matches_img = cv2.cvtColor(matches_img, cv2.COLOR_GRAY2BGR)
W = np.size(self.prevFrame, 1)
for m in goodMatches:
prev_pt = np.array(
self.prevKeyPoints[m.queryIdx].pt, dtype=np.int_)
curr_pt = np.array(keypoints[m.trainIdx].pt, dtype=np.int_)
curr_pt[0] += W
color = np.random.randint(0, 255, (3, ))
color = (int(color[0]), int(color[1]), int(color[2]))
matches_img = cv2.line(matches_img, prev_pt, curr_pt,
tuple(color), 1, cv2.LINE_AA)
matches_img = cv2.circle(matches_img, prev_pt, 2,
tuple(color), -1)
matches_img = cv2.circle(matches_img, curr_pt, 2,
tuple(color), -1)
plt.figure()
plt.imshow(matches_img)
plt.show()
# Find rigid matrix
if (np.size(prevPoints, 0) > 4) and (
np.size(prevPoints, 0) == np.size(prevPoints, 0)):
H, inliesrs = cv2.estimateAffinePartial2D(prevPoints, currPoints,
cv2.RANSAC)
# Handle downscale
if self.downscale > 1.0:
H[0, 2] *= self.downscale
H[1, 2] *= self.downscale
else:
print('Warning: not enough matching points')
# Store to next iteration
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
self.prevDescriptors = copy.copy(descriptors)
return H
def applySparseOptFlow(self, raw_frame, detections=None):
t0 = time.time()
# Initialize
height, width, _ = raw_frame.shape
frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2GRAY)
H = np.eye(2, 3)
# Downscale image
if self.downscale > 1.0:
# frame = cv2.GaussianBlur(frame, (3, 3), 1.5)
frame = cv2.resize(frame, (width // self.downscale,
height // self.downscale))
# find the keypoints
keypoints = cv2.goodFeaturesToTrack(
frame, mask=None, **self.feature_params)
# Handle first frame
if not self.initializedFirstFrame:
# Initialize data
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
# Initialization done
self.initializedFirstFrame = True
return H
if self.prevFrame.shape != frame.shape:
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
return H
# find correspondences
matchedKeypoints, status, err = cv2.calcOpticalFlowPyrLK(
self.prevFrame, frame, self.prevKeyPoints, None)
# leave good correspondences only
prevPoints = []
currPoints = []
for i in range(len(status)):
if status[i]:
prevPoints.append(self.prevKeyPoints[i])
currPoints.append(matchedKeypoints[i])
prevPoints = np.array(prevPoints)
currPoints = np.array(currPoints)
# Find rigid matrix
if (np.size(prevPoints, 0) > 4) and (
np.size(prevPoints, 0) == np.size(prevPoints, 0)):
H, inliesrs = cv2.estimateAffinePartial2D(prevPoints, currPoints,
cv2.RANSAC)
# Handle downscale
if self.downscale > 1.0:
H[0, 2] *= self.downscale
H[1, 2] *= self.downscale
else:
print('Warning: not enough matching points')
# Store to next iteration
self.prevFrame = frame.copy()
self.prevKeyPoints = copy.copy(keypoints)
t1 = time.time()
# gmc_line = str(1000 * (t1 - t0)) + "\t" + str(H[0, 0]) + "\t" + str(H[0, 1]) + "\t" + str(
# H[0, 2]) + "\t" + str(H[1, 0]) + "\t" + str(H[1, 1]) + "\t" + str(H[1, 2]) + "\n"
# self.gmc_file.write(gmc_line)
return H
def applyFile(self, raw_frame, detections=None):
line = self.gmcFile.readline()
tokens = line.split("\t")
H = np.eye(2, 3, dtype=np.float_)
H[0, 0] = float(tokens[1])
H[0, 1] = float(tokens[2])
H[0, 2] = float(tokens[3])
H[1, 0] = float(tokens[4])
H[1, 1] = float(tokens[5])
H[1, 2] = float(tokens[6])
return H
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