added dataset and made corrections

Signed-off-by: Suvaditya Mukherjee <suvadityamuk@gmail.com>

.gitattributes

@@ -37,3 +37,4 @@ openface.nn4.small2.v1.t7 filter=lfs diff=lfs merge=lfs -text
 res10_300x300_ssd_iter_140000.caffemodel filter=lfs diff=lfs merge=lfs -text
 unknownEmbeddings.pkl filter=lfs diff=lfs merge=lfs -text
 unknownNames.pkl filter=lfs diff=lfs merge=lfs -text
+celeb_dataset/** filter=lfs diff=lfs merge=lfs -text

app.py

@@ -13,15 +13,17 @@ from imutils import resize
 
 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]
-        f = open(f"/content/Celebrity Faces Dataset/{name}/{file}", "rb")
+        path = os.path.join(os.getcwd(), name, file)
+        # f = open(f"/celeb_dataset/'{name}'/{file}", "rb")
+        f = open(path, "rb")
         file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8)
         image = cv2.imdecode(file_bytes, 1)
         image = resize(image, width=600)
@@ -58,9 +60,9 @@ def calc_embeddings(all_files, names):
             knownNames.append(name)
             knownEmbeddings.append(vec.flatten())
         total += 1
-    with open("/content/unknownEmbeddings.pkl", "rb") as fp:
+    with open("../unknownEmbeddings.pkl", "rb") as fp:
         l = pickle.load(fp)
-    with open("/content/unknownNames.pkl", "rb") as fp:
+    with open("../unknownNames.pkl", "rb") as fp:
         n = pickle.load(fp)
     for i in l:
         knownEmbeddings.append(i)
@@ -80,7 +82,7 @@ def recognize(embeddings, names):
 
 def run_inference(myImage):
 
-    os.chdir("/content/Celebrity Faces Dataset")
+    os.chdir("celeb_dataset")
     celebs = []
     scores = dict()
 
@@ -96,12 +98,11 @@ def run_inference(myImage):
             names.append(name)
         embeddings, names = calc_embeddings(files, names)
         le, model = recognize(embeddings, names)
-
         detector = cv2.dnn.readNetFromCaffe(
-            "/content/deploy.prototxt.txt",
-            "/content/res10_300x300_ssd_iter_140000.caffemodel",
+            "../deploy.prototxt.txt",
+            "../res10_300x300_ssd_iter_140000.caffemodel",
         )
-        embedder = cv2.dnn.readNetFromTorch("/content/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)),