added new files and made changes in app.py

Signed-off-by: Suvaditya Mukherjee <[email protected]>

app.py

@@ -10,15 +10,15 @@ from sklearn.svm import SVC
 
 def calc_embeddings(all_files, names):
     detector = cv2.dnn.readNetFromCaffe(
-        "../deploy.prototxt.txt", "../res10_300x300_ssd_iter_140000.caffemodel"
+        "deploy.prototxt.txt", "res10_300x300_ssd_iter_140000.caffemodel"
     )
-    embedder = cv2.dnn.readNetFromTorch("../openface.nn4.small2.v1.t7")
+    embedder = cv2.dnn.readNetFromTorch("openface.nn4.small2.v1.t7")
     knownNames = []
     knownEmbeddings = []
     total = 0
     for file in all_files:
         name = names[total]
-        path = os.path.join(os.getcwd(), name, file)
+        path = os.path.join(os.getcwd(), 'celeb_dataset', name, file)
         # f = open(f"/celeb_dataset/'{name}'/{file}", "rb")
         f = open(path, "rb")
         file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8)
@@ -57,9 +57,9 @@ def calc_embeddings(all_files, names):
             knownNames.append(name)
             knownEmbeddings.append(vec.flatten())
         total += 1
-    with open("../unknownEmbeddings.pkl", "rb") as fp:
+    with open("unknownEmbeddings.pkl", "rb") as fp:
         l = pickle.load(fp)
-    with open("../unknownNames.pkl", "rb") as fp:
+    with open("unknownNames.pkl", "rb") as fp:
         n = pickle.load(fp)
     for i in l:
         knownEmbeddings.append(i)
@@ -79,27 +79,27 @@ def recognize(embeddings, names):
 
 def run_inference(myImage):
 
-    os.chdir("celeb_dataset")
+    # os.chdir("./celeb_dataset")
     celebs = []
     scores = dict()
 
-    for celeb in os.listdir():
+    for celeb in os.listdir("./celeb_dataset"):
         files = []
         names = []
         if celeb in celebs:
             continue
         name = celeb
         celebs.append(name)
-        for file in os.listdir(celeb):
+        for file in os.listdir(os.path.join(os.getcwd(), 'celeb_dataset', celeb)):
             files.append(file)
             names.append(name)
         embeddings, names = calc_embeddings(files, names)
         le, model = recognize(embeddings, names)
         detector = cv2.dnn.readNetFromCaffe(
-            "../deploy.prototxt.txt",
-            "../res10_300x300_ssd_iter_140000.caffemodel",
+            "deploy.prototxt.txt",
+            "res10_300x300_ssd_iter_140000.caffemodel",
         )
-        embedder = cv2.dnn.readNetFromTorch("../openface.nn4.small2.v1.t7")
+        embedder = cv2.dnn.readNetFromTorch("openface.nn4.small2.v1.t7")
         (h, w) = myImage.shape[:2]
         imageBlob = cv2.dnn.blobFromImage(
             cv2.resize(myImage, (300, 300)),
@@ -130,10 +130,11 @@ def run_inference(myImage):
                 j = np.argmax(preds)
                 proba = preds[j]
                 name = le.classes_[j]
-                # text = "{}: {:.2f}%".format(name, proba * 100)
-                scores[name] = proba * 100
-    result = sorted(scores.items(), key=lambda x: x[1], reverse=True)
-    return result
+                text = "{}: {:.2f}%".format(name, proba * 100)
+                scores[name] = proba
+    if len(scores)>1:
+        del scores["Unknown"]
+    return scores
 
 
 iface = gr.Interface(