get paraVISION working again

src/faceclass.py

@@ -252,7 +252,9 @@ class FaceClass(object):
 
     # @doc crop an image, allowing a gutter.
     def shrink(self, name, skale=0.5):
-        print ("shrinking ",name)
+        print ("shrinking ",name,skale)
+        if skale == 0:
+            return
         self.imgs[name] = cv2.resize(self.imgs[name],None,fx=skale,fy=skale)
 
 

src/paravisionx.py

@@ -3,21 +3,32 @@
 #
 import json
 import sys
+import os
+import cv2
+import torchvision.transforms
 
 ### export PYTHONPATH=/wherever/yoloserv/modules ... as long as "paravision/.../" is in there
 from paravision.recognition.exceptions import ParavisionException
-from paravision.recognition.engine import Engine
 from paravision.recognition.sdk import SDK
+from paravision.recognition.engine import Engine
 import paravision.recognition.utils as pru
-#from openvino.inference_engine import Engineq
+ #from openvino.inference_engine import Engineq
 
 #from deepface.basemodels import VGGFace, OpenFace, Facenet, FbDeepFace, DeepID
 from faceclass import FaceClass
 
 
- 
+#  Paravision is quite unlike all the other open source img recognition packages
+#   which cause problems when image formats move between the two.
+#   So this class has quite a few differences. 
 class Paravision(FaceClass):
 
+    models = {}
+    scores = {}
+    quality = {}
+    sdk = None
+    match_score = 0
+
     def init(self,backend=None,model=None):
         print("@@@ initialising paravision")
         try:
@@ -26,6 +37,18 @@ class Paravision(FaceClass):
             pass
 
 
+    # @doc Load a pic using the device label
+    def load1(self, name,fname):
+        print(" Loading image '%s' from file %s" %  (name, fname))
+        if not os.path.isfile(fname):
+            print(" * file not found: %s" %  (fname))
+            return  '{ "status":442565, "remark":"file name not found", "guilty_param":"fname", "guilty_value":"%s" }' % (fname)
+        self.files[name] = fname
+        self.imgs[name] = pru.load_image(fname)
+        print(" Loaded %s from file %s" %  (name, fname))
+        return '{ "status":0, "remark":"OK", "name":"%s", "fname":"%s" }' % (name,fname)
+
+
     # @doc find all the faces in the named image
     def detect(self, name):
         boxes = []
@@ -33,46 +56,58 @@ class Paravision(FaceClass):
         print("** face_recognition::detect ... %s" % name)     
         try:
             # Get all faces from images with qualities, landmarks, and embeddings
-            res = self.sdk.get_faces([self.imgs[name]])
+            faces = self.sdk.get_faces([self.imgs[name]], qualities=True, landmarks=True, embeddings=True)
-            boxes = res.faces
+            inferences = faces.image_inferences
-            for b in boxes:
+            ix = inferences[0].most_prominent_face_index()
-                b = b.bounding_box
+            self.models[name] = inferences[0].faces[ix].embedding
-                #print(b)
+            self.quality[name] = round(1000*inferences[0].faces[ix].quality)
-                self.boxes.append((int(b.origin.x),int(b.origin.y),int(b.origin.x+b.width),int(b.origin.y+b.height)))
+            self.boxes = [ (0,0,0,0) ]
-            print("found %d boxes for %s" % (len(self.boxes), name) )
         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))
 
 
+    # @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):
+        print("##1##")
+        if self.json2obj(self.load1(name1, file1))["status"] != 0:
+            return self.jsonx
+        if self.json2obj(self.detect(name1))["status"] != 0:
+            return self.jsonx
+        self.save(name1,"/tmp")
+
+        print("##2##")
+        if self.json2obj(self.load1(name2, file2))["status"] != 0:
+            return self.jsonx
+        self.save(name2,"/tmp")
+        if self.json2obj(self.detect(name2))["status"]!=0:
+            return self.jsonx
+        self.save(name2,"/tmp")
+
+        print("##R##")        
+        jsonstr = self.compare(name1,name2)
+        print(jsonstr)
+        return jsonstr
+
 
     # @doc compare two named images, previously loaded
-    def compare(self, name1, name2):   
+    def compare(self, name1, name2):           
         print("** face_recognition::compare ... %s vs %s" % (name1,name2))
-        self.encs[name1] = self.sdk.get_embedding_from_prepared_image(self.imgs[name1])
-        self.encs[name2] = self.sdk.get_embedding_from_prepared_image(self.imgs[name2])
-        #if self.encs[name1]==[]:
-        #    return '{ "status":14330, "remark":"could not encode image", "guilty_param":"img1", "guilty_value":"%s" }' % name1 
-        #if self.encs[name2]==[]:
-        #    return '{ "status":14331, "remark":"could not encode image", "guilty_param":"img2", "guilty_value":"%s" }' % name2 
-        print(self.encs.keys())
-        print(self.encs[name1])
-        print(self.encs[name2])
         try:
-            res = self.sdk.get_match_score(self.model, other.model)
+            res = self.sdk.get_match_score(self.models[name1], self.models[name2])
             print("Match is ",res)
-            #self.match_score = 1000 * (1 - face_recognition.face_distance([self.encs[name1][0]], self.encs[name2][0]))            
+            self.match_score = res
-            #print("Score is ",self.match_score)
-            
-            # Create .json
-            self.tree["score"] = self.match_score[0]
         except Exception as ex:
             print("** paravision::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] 
+        return '{ "status":0, "remark":"OK", "score":%d }' % self.match_score
 
+
+    def scores(self):
+        return  '{ "status":0, "remark":"OK", "score":%d }' % self.match_score
    
 
 

src/yoloserv.py

@@ -240,12 +240,12 @@ class yoloserv(object):
 
     # Match faces together
     @cherrypy.expose
-    def svc_match_faces(self,dev1,fil1,scl1,dev2,fil2,scl2):
+    def svc_match_faces(self,dev1,fil1,scl1s,dev2,fil2,scl2s):
-        jsonstr = self.facematcher.crowd_vs_govid(dev1,self.conf["yolo_indir"]+fil1,scl1, dev2,self.conf["yolo_outdir"]+fil2,scl2)
+        scl1 = float(scl1s)
+        scl2 = float(scl2s)
+        jsonstr = self.facematcher.crowd_vs_govid(dev1,self.conf["yolo_indir"]+fil1,scl1, dev2,self.conf["yolo_indir"]+fil2,scl2)
         obj = self.json2obj(jsonstr)
         return jsonstr
-        if obj.status > 0:
-            return jsonstr
  
     def json2obj(self,jsonx):
         return json.loads(jsonx)