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get around weird face_recognition issue re BBR vs RGB images for encodings

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carl 2024-09-05 11:57:04 -03:00
parent cb8fd60779
commit 4276a46e25
4 changed files with 119 additions and 85 deletions
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1
sbin/ctl.sh
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@ -100,6 +100,7 @@ do
"face_recognition") LLP="$LLP:$LIB_DEEPFACE"
PYP="$PYP:$PYP_DEEPFACE"
;;
"regula") ;;
"camera") ;;
*) echo "yoloserv does not implement backend $i. Edit /etc/ukdi.json::yolo_devices and try again."
exit 1

49
src/face_recognitionx.py
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@ -56,12 +56,13 @@ class FaceRecognition(FaceClass):
face_match_json = None
conf = []
def init(self,backend=None,model=None):
model = load_model("./emotion_detector_models/model.hdf5")
#def init():
#model = load_model("./emotion_detector_models/model.hdf5")
# @doc find all the faces in the named image
def detect(self, name):
self.tree["detectms"] = time.time()
boxes = []
self.boxes = []
print("** face_recognition::detect ... %s" % name)
@ -77,7 +78,8 @@ class FaceRecognition(FaceClass):
except Exception as ex:
self.errstr = "image processing exception at get_faces: "+str(ex)
return '{ "status":222310, "remark":"image processing exception", "guilty_param":"error", "guilty_value":"%s" }' % str(ex)
return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s }' % (len(self.boxes), json.dumps(self.boxes))
self.tree["detectms"] = time.time() - self.tree["detectms"]
return '{ "status":0, "remark":"OK", "faces":%d, "boxes":%s, "time":%d }' % (len(self.boxes), json.dumps(self.boxes), self.tree["detectms"] )
# @doc find the landmarks of the given face
@ -102,9 +104,23 @@ class FaceRecognition(FaceClass):
# @doc compare two named images, previously loaded
def compare(self, name1, name2):
self.tree["comparems"] = time.time()
print("** face_recognition::compare ... %s vs %s" % (name1,name2))
self.encs[name1] = face_recognition.face_encodings(self.imgs[name1])
self.encs[name2] = face_recognition.face_encodings(self.imgs[name2])
print("images available = ",self.imgs.keys())
print(">>>>>>>>>>>>>>>> "+name1, self.imgs[name1])
# CCG 2024-09-05 Convert images to RGB so face_recognition can make encodings.
# Oddly, this is only needed on NUC (SMRT08) and work OK at home with the 2 commented lines below.
# Not sure why. Could be a lib thing. Worth keeping an eye on.
rgb = cv2.cvtColor(self.imgs[name1], cv2.COLOR_BGR2RGB)
self.encs[name1] = face_recognition.face_encodings(rgb)
rgb = cv2.cvtColor(self.imgs[name2], cv2.COLOR_BGR2RGB)
self.encs[name2] = face_recognition.face_encodings(rgb)
#self.encs[name1] = face_recognition.face_encodings(self.imgs[name1])
#self.encs[name2] = face_recognition.face_encodings(self.imgs[name2])
print("encodings available = ",self.encs.keys())
if self.encs[name1]==[]:
return '{ "status":14330, "remark":"could not encode image", "guilty_param":"img1", "guilty_value":"%s" }' % name1
if self.encs[name2]==[]:
@ -124,7 +140,8 @@ class FaceRecognition(FaceClass):
print("** face_recognition::compare exception ... " + str(ex) )
self.errstr = "image comparison exception at compute_scores: "+str(ex)
return '{ "status":332410, "remark":"%s" }' % self.errstr
return '{ "status":0, "remark":"OK", "score":%d }' % self.match_score[0]
self.tree["comparems"] = time.time() - self.tree["comparems"]
return '{ "status":0, "remark":"OK", "score":%d, "detectms":%d, "comparems":%d }' % (self.match_score[0], self.tree["detectms"]*1000, self.tree["comparems"]*1000 )
@ -138,6 +155,26 @@ if __name__ == '__main__':
print(jsonstr)
if sys.argv[1]=="messi":
d.load2("messi4","testimg/messi4.jpg","messi2","testimg/messi2.jpg")
d.detect("messi4")
d.boxscale("messi4",0.3)
d.ideal("messi4","messi4_rect","messi4_crop")
d.detect("messi2")
d.boxscale("messi2",0.3)
d.ideal("messi2","messi2_rect","messi2_crop")
d.save("messi4","/tmp")
d.save("messi4_rect","/tmp")
d.save("messi4_crop","/tmp")
d.save("messi2","/tmp")
d.save("messi2_rect","/tmp")
d.save("messi2_crop","/tmp")
print("XXXXXXXXXXXXXXXXXXXXXXXX")
d.stats("messi2_rect")
d.stats("messi2_crop")
d.compare("messi4_crop","messi2_rect")
if sys.argv[1]=="crowd":
jsonstr = d.crowd_vs_govid("pic1", "testimg/messi4.jpg", 0, "pic2", "testimg/messi2.jpg", 0)
print(jsonstr)

76
src/faceclass.py
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@ -4,12 +4,15 @@ import cv2
import json
import base64
import time
import numpy as np
from copy import copy, deepcopy
from matplotlib import pyplot as plt
class FaceClass(object):
backend = None
model = None
imgs = {}
encs = {}
faces = {}
@ -17,7 +20,7 @@ class FaceClass(object):
files = {}
boxes = []
errstr = ""
tree = { "device1":"NA", "device2":"NA", "threshold":380, "device1_qual":0.5, "device2_qual":0.5, "score":0 }
tree = { "device1":"NA", "device2":"NA", "threshold":380, "device1_qual":0.5, "device2_qual":0.5, "score":0, "detectms":0, "comparems":0 }
# # # # #####
# ## # # #
@ -29,6 +32,8 @@ class FaceClass(object):
# Prep tasks
def init(self, backend=None, mod=None):
self.backend = backend
self.model = mod
return None
def clear(self):
@ -82,7 +87,7 @@ class FaceClass(object):
def ideal(self, name, rectname, cropname):
found = -1
biggest = -1
print("** faceclass::ideal ... %s with %d boxes" % (name, len(self.boxes) ))
print("** faceclass::ideal ... %s with %d boxes => %s + %s" % (name, len(self.boxes), rectname, cropname ))
# resize boxes
for i in range(len(self.boxes)):
b = self.boxes[i]
@ -125,21 +130,14 @@ class FaceClass(object):
def compare(self,name1,name2):
return '{ "status":88245, "remark":"override this!" }'
def detect_all(self):
n=0
self.load("localcam", "/tmp/localcam.png", "regula", "/tmp/regula/Portrait_0.jpg")
self.get_faces("localcam")
self.get_faces("regula")
self.get_base_face()
print("computed ... ", d.compute_scores("regula","localcam"))
print("scores ... " , d.get_scores())
# @doc This does everything for you. If you are smartserv, "crowd" means cam and "govid" means regula pic
def crowd_vs_govid(self, name1,file1,scale1, name2,file2,scale2):
# @doc This does everything for you.
# If you are smartserv, "crowd" means cam and "govid" means regula pic
def crowd_vs_govid(self, name1,file1,scale1str, name2,file2,scale2str):
print("##1##")
t = time.time()
print ("///t = ", time.time() - t)
scale1 = float(scale1str)
scale2 = float(scale2str)
self.load1(name1, file1)
if scale1 !=0:
self.shrink(name1,scale1)
@ -147,15 +145,13 @@ class FaceClass(object):
if json.loads(jsonstr)["status"]!=0:
return jsonstr
self.boxscale(name1,0.3)
self.ideal(name1,"pic1_rect","pic1_crop")
self.ideal(name1,name1+"_rect",name1+"_crop")
self.save(name1,"/tmp")
self.save("pic1_rect","/tmp")
self.save("pic1_crop","/tmp")
self.save(name1+"_rect","/tmp")
self.save(name1+"_crop","/tmp")
print(self.imgs.keys())
print ("///t = ", time.time() - t)
print("##2##")
print ("///t = ", time.time() - t)
self.load1(name2, file2)
if scale2 !=0:
self.shrink(name2,scale2)
@ -164,17 +160,15 @@ class FaceClass(object):
if json.loads(jsonstr)["status"]!=0:
return jsonstr
self.boxscale(name2,0.3)
self.ideal(name2,"pic2_rect","pic2_crop")
self.ideal(name2,name2+"_rect",name2+"_crop")
self.save(name2,"/tmp")
self.save("pic2_rect","/tmp")
self.save("pic2_crop","/tmp")
self.save(name2+"_rect","/tmp")
self.save(name2+"_crop","/tmp")
print(self.imgs.keys())
print ("///t = ", time.time() - t)
print("##R##")
jsonstr = self.compare("pic1_crop","pic2_crop")
jsonstr = self.compare(name1+"_crop",name2+"_crop")
print(jsonstr)
print ("///t = ", time.time() - t)
###### ##### # #####
@ -210,7 +204,7 @@ class FaceClass(object):
# @doc draw a box on an image
def rect(self, name, x1, y1, x2, y2, color):
#print ("recting ",x1,y1,x2,y2)
print ("recting ",x1,y1,x2,y2)
cv2.rectangle(self.imgs[name],(x1,y1),(x2,y2), color, 4)
# @doc crop an image, allowing a gutter.
@ -223,6 +217,8 @@ class FaceClass(object):
print ("shrinking ",name)
self.imgs[name] = cv2.resize(self.imgs[name],None,fx=skale,fy=skale)
##### ###### ##### # # ##### # #
# # # # # # # # ## #
# # ##### # # # # # # # #
@ -242,22 +238,26 @@ class FaceClass(object):
def save(self,which,dir,format="png"):
cv2.imwrite(dir + "/" + which + "." + format, self.imgs[which])
def stats(self,which):
print(which + " stats....")
print(type(self.imgs[which]))
print(np.shape(self.imgs[which]))
if __name__ == '__main__':
# tests
# big/clear big/clear small/side small/clear obscured ig card IR cam some rotated
picfiles = [ "gerrard.jpg", "ronaldo.jpg", "messi1.jpg", "messi2.jpg", "ox_matt.png", "ox_govid.jpg", "cg_ir.jpg", "fivepeople.jpg" ]
# Test the dlib image detector
d = FaceClass()
# quick test
if sys.argv[1]=="quick":
d.visual = 1
d.load("messi1","messi2","testimg/messi1.jpg","testimg/messi2.jpg")
sys.exit(0)
d.load1("messi1","testimg/messi1.jpg")
d.load2("messi2","testimg/messi2.jpg", "messi3","testimg/messi3.jpg")
# look for pics in /tmp
d.save("messi1", "/tmp")
d.save("messi2", "/tmp")
d.save("messi3", "/tmp")

72
src/yoloserv.py
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@ -59,15 +59,15 @@ class yoloserv(object):
# Object (face) matching
if "paravision" in self.devices:
print("Loading paravision facematch...")
from para_facematch import Facematch
self.facematcher = Facematch()
from paravisionx import Paravision
self.facematcher = Paravision()
self.facematcher.init()
if "deepface" in self.devices:
print("Loading deepface facematch...")
from deepfacex import Deepfacex
self.facematcher = Deepfacex()
self.facematcher.init("dlib","SFace")
self.facematcher.init("dlib","Facenet512")
if "face_recognition" in self.devices:
print("Loading face_recognition facematch...")
@ -76,7 +76,7 @@ class yoloserv(object):
self.facematcher.init()
# Object (face) matching
if "facematch_open" in self.devices:
if "facematch" in self.devices:
print("Loading paravision facematch...")
from facematch_open import Facematch
self.facematcher = Facematch()
@ -130,7 +130,6 @@ class yoloserv(object):
# @doc clear memory of all image data
@cherrypy.expose
def svc_init(self):
self.facematcher.init()
return '{ "status":0, "remark":"OK" }'
# @doc Acquire image -
@ -186,9 +185,8 @@ class yoloserv(object):
return self.facematcher.load2(name1, self.indir + infile1, name2, self.indir + infile2)
@cherrypy.expose
def svc_detect_faces(self,infile):
nfaces = self.facematcher.detect_all(self.indir + infile)
return '{ "status":0, "remark":"found faces", "count":%d }' % (nfaces)
def svc_detect_faces(self,name):
return self.facematcher.detect(name)
# @doc find all the faces in the named image that was loaded using the above calls (test OK CG 2024-0724)
@cherrypy.expose
@ -196,10 +194,10 @@ class yoloserv(object):
return self.facematcher.get_faces(which)
# @doc find the most prominent face in the named image that was loaded using the above calls (test OK CG 2024-0724)
# you can access the new ideal face (if present) with the image name "_ideal"
# you can access the new ideal face (if present) with the image name "_crop"
@cherrypy.expose
def svc_ideal(self,which,rectname,cropname):
return self.facematcher.get_ideal(which,rectname,cropname)
def svc_ideal(self,which):
return self.facematcher.ideal(which,which+"_rect",which+"_crop")
# @doc dumps the named image as an <img> tag for straight to HTML output (test OK CG 2024-0724)
@cherrypy.expose
@ -207,18 +205,15 @@ class yoloserv(object):
buf = self.facematcher.dump64(which).decode()
return "<html><img src='%s'/></html>" % buf
@cherrypy.expose
def svc_save_face(self,which):
return self.facematcher.save(which, self.conf["yolo_outdir"]);
# Match faces together
@cherrypy.expose
def svc_compare(self,name1,name2):
return self.facematcher.compare(name1,name2)
@cherrypy.expose
def svc_facematch(self,name1,infile1,name2,infile2):
self.facematcher.init()
return self.facematcher.get_faces(name1)
return self.facematcher.get_faces(name2)
self.facematcher.load2(name1, self.indir + infile1, name2, self.indir + infile2)
return self.facematcher.compare(name1,name2)
@cherrypy.expose
@ -237,14 +232,19 @@ class yoloserv(object):
# Match faces together
@cherrypy.expose
def svc_match_faces(self,infile1,infile2):
return self.facematcher.detect(infile1,infile2)
def svc_match_faces(self,dev1,fil1,scl1,dev2,fil2,scl2):
jsonstr = self.facematcher.crowd_vs_govid(dev1,self.conf["yolo_indir"]+fil1,scl1, dev2,self.conf["yolo_outdir"]+fil2,scl2)
obj = self.json2obj(jsonstr)
return jsonstr
if obj.status > 0:
return jsonstr
def json2obj(self,jsonx):
return json.laods(jsonx)
# @doc put all the steps for a retail facematch into one convenient functions
@cherrypy.expose
def retail_facematch(self,dev1,dev2):
def svc_facematch(self,dev1,dev2):
if self.facematcher is None:
return '{ "status":777244, "remark":"suitable yolo_device" }'
@ -252,29 +252,25 @@ class yoloserv(object):
return '{ "status":0, "remark":"OK(Emulated)", "data":{"device1":"%s","device2":"%s","device1_qual":123,"device2_qual":234,"score":600} }' % (dev1,dev2)
if dev1 == "regula":
img1 = "/tmp/regula/Portrait_0.jpg"
fil1 = "/tmp/regula/Portrait_0.jpg"
scl1 = 0.25
if dev1 == "localcam":
img1 = "/tmp/localcam.png"
fil1 = "/tmp/localcam.png"
scl1 = 0.5
if dev2 == "regula":
img2 = "/tmp/regula/Portrait_0.jpg"
fil2 = "/tmp/regula/Portrait_0.jpg"
scl2 = 0.25
if dev2 == "localcam":
img2 = "/tmp/localcam.png"
fil2 = "/tmp/localcam.png"
scl2 = 0.5
if self.conf["emulate_facematch"]:
return '{ "status":0, "remark":"OK", "data":{} }'
obj = self.json2obj(self.facematcher.load_faces(dev1, img1, dev2, img2))
if obj["status"] > 0:
return jsonx
obj = self.json2obj(self.facematcher.get_faces(dev1))
if obj["status"] > 0:
return jsonx
obj = self.json2obj(self.facematcher.get_faces(dev2))
if obj["status"] > 0:
return jsonx
obj = self.json2obj(self.facematcher.compute_scores(dev1,dev2))
if obj["status"] > 0:
return jsonx
jsonstr = self.facematcher.get_scores()
jsonstr = self.facematcher.crowd_vs_govid(dev1,fil1,scl1, dev2,fil2,scl2)
obj = self.json2obj(jsonstr)
if obj.status > 0:
return jsonstr
jsonstr = self.facematcher.scores()
return '{ "status":0, "remark":"OK", "data": %s }' % (jsonstr)