Initial commit
This commit is contained in:
Derek Schmidt
commit
2b8ee92b62
5 changed files with 593 additions and 0 deletions
3
.gitmodules
vendored
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3
.gitmodules
vendored
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@ -0,0 +1,3 @@
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[submodule "head_pose_estimation"]
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path = head_pose_estimation
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url = https://github.com/yinguobing/head-pose-estimation.git
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12
Pipfile
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12
Pipfile
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@ -0,0 +1,12 @@
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[[source]]
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name = "pypi"
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url = "https://pypi.org/simple"
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verify_ssl = true
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[dev-packages]
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[packages]
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face-alignment = {git = "https://git.skeh.site/skeh/face-alignment.git"}
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[requires]
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python_version = "3.7"
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428
Pipfile.lock
generated
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428
Pipfile.lock
generated
Normal file
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@ -0,0 +1,428 @@
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{
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"_meta": {
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"hash": {
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"sha256": "1adba996d38a87bfbbb10ae7347cd16844192e7a465bbd0630d5858e076fd54c"
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},
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"pipfile-spec": 6,
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"requires": {
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"python_version": "3.7"
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},
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"sources": [
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{
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"name": "pypi",
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"url": "https://pypi.org/simple",
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"verify_ssl": true
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}
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]
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},
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"default": {
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"cycler": {
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"hashes": [
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"sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d",
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"sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"
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],
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"version": "==0.10.0"
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},
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"decorator": {
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"hashes": [
|
||||
"sha256:41fa54c2a0cc4ba648be4fd43cff00aedf5b9465c9bf18d64325bc225f08f760",
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"sha256:e3a62f0520172440ca0dcc823749319382e377f37f140a0b99ef45fecb84bfe7"
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],
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"version": "==4.4.2"
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},
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"face-alignment": {
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"git": "https://git.skeh.site/skeh/face-alignment.git",
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"hashes": [
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"sha256:cc35c1805fad5f7ea75df265a0d3b7d8252f017a105757366e2c65bd2e12b4e4"
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],
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"ref": "7b903952d3fd1625e6fbe34cf70f83331964a210"
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},
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"imageio": {
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"hashes": [
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"version": "==2.9.0"
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||||
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"kiwisolver": {
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|
||||
],
|
||||
"markers": "python_version >= '3.6'",
|
||||
"version": "==1.3.1"
|
||||
},
|
||||
"llvmlite": {
|
||||
"hashes": [
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||||
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||||
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||||
"matplotlib": {
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
"sha256:2e49cac969976be63117004ee00d0a3e3dd4ea662ad77383f671b8992825de1a",
|
||||
"sha256:38d67f4fb189a92a977b2c0a38e4f6dd413e0bf55aa6d40004696df7e40a71ff",
|
||||
"sha256:422e64e98d0e100c360993819d0307e5d56e9517b26135808ad68984d577d75a",
|
||||
"sha256:5d76c255a41484c1d41a9ff570b9c9f36cb85df9428aa15a58ae16ac7cfc2ea6",
|
||||
"sha256:6652a767a0572ae0feb74ad128758e507afd3b8396b6e7f147e438ba8d4c6f63",
|
||||
"sha256:a3793dcceb12b1e2281290cca1277c5ce86ddfd5bf044f654285a4d69057aea7",
|
||||
"sha256:af464a6f4314a875035e0c4c2b07517599704b214634f4ed3ad2e748c5ef291f",
|
||||
"sha256:d241c3f1c4d563e4ba86f84769c23e12606db167ee6f674eedff6d02901462e3",
|
||||
"sha256:dd2fc6880c95e836960d86efbbc7f63d3287f2e1893c51d31f96dbfe02f0d73e",
|
||||
"sha256:de84b4166e3f7335eb868b51d3bbd909ec33828af27290b4171bce832a55be3c",
|
||||
"sha256:e000b94be3aa58ad7f61e7d07cf379ea9366cf6c6874e68bd58ad0bdc537b3a7",
|
||||
"sha256:f0aaf657145533824b15f2fd8fde8f8c67fe6c6281088ef588091f03fad90243"
|
||||
],
|
||||
"markers": "python_full_version >= '3.6.2'",
|
||||
"version": "==1.7.1"
|
||||
},
|
||||
"tqdm": {
|
||||
"hashes": [
|
||||
"sha256:4621f6823bab46a9cc33d48105753ccbea671b68bab2c50a9f0be23d4065cb5a",
|
||||
"sha256:fe3d08dd00a526850568d542ff9de9bbc2a09a791da3c334f3213d8d0bbbca65"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
||||
"version": "==4.56.0"
|
||||
},
|
||||
"typing-extensions": {
|
||||
"hashes": [
|
||||
"sha256:7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918",
|
||||
"sha256:99d4073b617d30288f569d3f13d2bd7548c3a7e4c8de87db09a9d29bb3a4a60c",
|
||||
"sha256:dafc7639cde7f1b6e1acc0f457842a83e722ccca8eef5270af2d74792619a89f"
|
||||
],
|
||||
"version": "==3.7.4.3"
|
||||
}
|
||||
},
|
||||
"develop": {}
|
||||
}
|
||||
1
head_pose_estimation
Submodule
1
head_pose_estimation
Submodule
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 981cac645ea2618d25325870c95e62a6f0595ef4
|
||||
149
main.py
Normal file
149
main.py
Normal file
|
|
@ -0,0 +1,149 @@
|
|||
import face_alignment
|
||||
import face_alignment.detection.blazeface
|
||||
from threading import Thread
|
||||
from queue import LifoQueue, Queue, Full as FullException, Empty as EmptyException
|
||||
|
||||
from posehead-pose-estimation.pose_estimator import PoseEstimator
|
||||
|
||||
import cv2
|
||||
|
||||
|
||||
class FaceDetector(Thread):
|
||||
def __init__(self, frame_queue, track_queue):
|
||||
super().__init__()
|
||||
self.alive = True
|
||||
|
||||
self._frame_queue = frame_queue
|
||||
self._track_queue = track_queue
|
||||
|
||||
self._face_aligner = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cpu')
|
||||
self._face_detector = face_alignment.detection.blazeface.FaceDetector(device='cpu')
|
||||
|
||||
def find_face(self, frame):
|
||||
smol_frame = cv2.resize(frame, None, fx=0.5, fy=0.5)
|
||||
detections = self._face_detector.detect_from_image(frame)
|
||||
if (len(detections) == 0):
|
||||
return None
|
||||
else:
|
||||
return (detections[0]).astype(int)
|
||||
|
||||
def get_landmarks(self, frame, face_box):
|
||||
landmarks = self._face_aligner.get_landmarks_from_image(frame, detected_faces=[face_box])
|
||||
if len(landmarks) == 0:
|
||||
return None
|
||||
else:
|
||||
return landmarks[0]
|
||||
|
||||
def run(self):
|
||||
while self.alive:
|
||||
newest_frame = self._frame_queue.get()
|
||||
face_box = self.find_face(newest_frame)
|
||||
if face_box is None:
|
||||
continue
|
||||
landmarks = self.get_landmarks(newest_frame, face_box)
|
||||
if face_box is None:
|
||||
continue
|
||||
self._track_queue.put((landmarks, newest_frame))
|
||||
self._track_queue.join()
|
||||
|
||||
|
||||
|
||||
class FaceTracker3000:
|
||||
def __init__(self, fps, width, height, bufferlen):
|
||||
self._bufferlen = bufferlen
|
||||
|
||||
# Setup video capture
|
||||
self._cap = cv2.VideoCapture(1, cv2.CAP_V4L)
|
||||
self._cap.set(cv2.CAP_PROP_FPS, fps)
|
||||
self._cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
|
||||
self._cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
|
||||
|
||||
# Setup queues
|
||||
self.frame_queue = Queue(maxsize=bufferlen)
|
||||
self.track_queue = Queue(maxsize=bufferlen)
|
||||
self.output_queue = Queue(maxsize=bufferlen)
|
||||
|
||||
# Setup secondary ml thread
|
||||
self._face_detector_thread = FaceDetector(self.frame_queue, self.track_queue)
|
||||
self._face_detector_thread.daemon = True
|
||||
|
||||
# Setup Lucas-Kanade sparse optical flow paramaters
|
||||
# TODO: learn what these should do and make them easier to configure
|
||||
self.lk_params = dict( winSize = (10,10),
|
||||
maxLevel = 2,
|
||||
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
|
||||
|
||||
def run(self):
|
||||
# Start thread for intensive ML jobs (will read from frame_queue)
|
||||
self._face_detector_thread.start()
|
||||
try:
|
||||
while True:
|
||||
# get frame
|
||||
success, current_frame = self._cap.read()
|
||||
|
||||
# Place this frame in the queue if we have room, otherwise drop it
|
||||
try:
|
||||
self.frame_queue.put_nowait(current_frame)
|
||||
except FullException:
|
||||
pass
|
||||
|
||||
# If we have some landmarks from the ML thread
|
||||
while not self.track_queue.empty():
|
||||
# Get the landmarks and place them in the output queue
|
||||
landmarks, landmark_frame = self.track_queue.get()
|
||||
self.output_queue.put((landmark_frame, landmarks, False))
|
||||
|
||||
# Interpolate the rest of our buffer
|
||||
for frame, interpolated_landmarks in self.interpolate_from(landmarks, landmark_frame, **self.lk_params):
|
||||
try:
|
||||
self.output_queue.put_nowait((frame, interpolated_landmarks, True))
|
||||
except FullException:
|
||||
break
|
||||
if not self.track_queue.empty():
|
||||
# Let the ML thread know its ok to choose another frame (we've emptied the queue)
|
||||
self.track_queue.task_done()
|
||||
|
||||
# Display the frames we have ready to go
|
||||
if not self.output_queue.empty():
|
||||
frame, landmarks, interpolated = self.output_queue.get_nowait()
|
||||
if landmarks is not None:
|
||||
color = (255, 255, 255) if interpolated else (0, 255, 0)
|
||||
for mark in landmarks:
|
||||
cv2.circle(frame, (int(mark[0]), int(mark[1])), 1, color, -1, cv2.LINE_AA)
|
||||
cv2.imshow('lk_track', frame)
|
||||
ch = cv2.waitKey(1)
|
||||
if ch == 27:
|
||||
break
|
||||
|
||||
# Cleanup our thread / cv2 windows if neccesary
|
||||
except Exception as e:
|
||||
self.track_queue.task_done()
|
||||
self._face_detector_thread.alive = False
|
||||
self._face_detector_thread.join()
|
||||
raise e
|
||||
finally:
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
# Uses Lucas-Kanade sparse optical flow to attempt to interpolate tracking data
|
||||
def interpolate_from(self, landmarks, landmark_frame, **lk_params):
|
||||
old_frame = cv2.cvtColor(landmark_frame, cv2.COLOR_BGR2GRAY)
|
||||
old_points = landmarks
|
||||
while not self.frame_queue.empty():
|
||||
try:
|
||||
new_frame_raw = self.frame_queue.get_nowait()
|
||||
except EmptyException:
|
||||
break
|
||||
new_frame = cv2.cvtColor(new_frame_raw, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
new_points, statuses, errors = cv2.calcOpticalFlowPyrLK(old_frame, new_frame, old_points, None, **lk_params)
|
||||
if any(point_status == 0 or point_error > 15 for point_status, point_error in zip(statuses, errors)):
|
||||
yield new_frame_raw, None
|
||||
else:
|
||||
yield new_frame_raw, new_points
|
||||
|
||||
old_points = new_points
|
||||
old_frame = new_frame
|
||||
|
||||
if __name__ == '__main__':
|
||||
ft = FaceTracker3000(30, 640, 480, 100)
|
||||
ft.run()
|
||||
Loading…
Add table
Reference in a new issue