new files added and lfs used

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

.gitattributes

@@ -32,3 +32,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+deploy.prototxt.txt filter=lfs diff=lfs merge=lfs -text
+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

app.py

@@ -0,0 +1,149 @@
+import gradio as gr
+import numpy as np
+import cv2
+import os
+from imutils import resize
+import pickle
+from sklearn.preprocessing import LabelEncoder
+from sklearn.svm import SVC
+import numpy as np
+import cv2
+from imutils import resize
+
+
+def calc_embeddings(all_files, names):
+    detector = cv2.dnn.readNetFromCaffe(
+        "deploy.prototxt.txt", "res10_300x300_ssd_iter_140000.caffemodel"
+    )
+    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")
+        file_bytes = np.asarray(bytearray(f.read()), dtype=np.uint8)
+        image = cv2.imdecode(file_bytes, 1)
+        image = resize(image, width=600)
+        (h, w) = image.shape[:2]
+
+        imageBlob = cv2.dnn.blobFromImage(
+            cv2.resize(image, (300, 300)),
+            1.0,
+            (300, 300),
+            (104.0, 177.0, 123.0),
+            swapRB=False,
+            crop=False,
+        )
+        detector.setInput(imageBlob)
+        detections = detector.forward()
+
+        if len(detections) > 0:
+            i = np.argmax(detections[0, 0, :, 2])
+            confidence = detections[0, 0, i, 2]
+
+        if confidence > 0.5:
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            (startX, startY, endX, endY) = box.astype("int")
+            face = image[startY:endY, startX:endX]
+            (fH, fW) = face.shape[:2]
+            if fW < 20 or fH < 20:
+                continue
+
+            faceBlob = cv2.dnn.blobFromImage(
+                face, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False
+            )
+            embedder.setInput(faceBlob)
+            vec = embedder.forward()
+            knownNames.append(name)
+            knownEmbeddings.append(vec.flatten())
+        total += 1
+    with open("/content/unknownEmbeddings.pkl", "rb") as fp:
+        l = pickle.load(fp)
+    with open("/content/unknownNames.pkl", "rb") as fp:
+        n = pickle.load(fp)
+    for i in l:
+        knownEmbeddings.append(i)
+    knownNames = knownNames + n
+    return knownEmbeddings, knownNames
+
+
+def recognize(embeddings, names):
+
+    le = LabelEncoder()
+    labels = le.fit_transform(names)
+    recognizer = SVC(C=1.0, kernel="linear", probability=True)
+    recognizer.fit(embeddings, names)
+
+    return le, recognizer
+
+
+def run_inference(myImage):
+
+    os.chdir("/content/Celebrity Faces Dataset")
+    celebs = []
+    scores = dict()
+
+    for celeb in os.listdir():
+        files = []
+        names = []
+        if celeb in celebs:
+            continue
+        name = celeb
+        celebs.append(name)
+        for file in os.listdir(celeb):
+            files.append(file)
+            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",
+        )
+        embedder = cv2.dnn.readNetFromTorch("/content/openface.nn4.small2.v1.t7")
+        (h, w) = myImage.shape[:2]
+        imageBlob = cv2.dnn.blobFromImage(
+            cv2.resize(myImage, (300, 300)),
+            1.0,
+            (300, 300),
+            (104.0, 177.0, 123.0),
+            swapRB=False,
+            crop=False,
+        )
+        detector.setInput(imageBlob)
+        detections = detector.forward()
+        for i in range(0, detections.shape[2]):
+            confidence = detections[0, 0, i, 2]
+            if confidence > 0.5:
+                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+                (startX, startY, endX, endY) = box.astype("int")
+                face = myImage[startY:endY, startX:endX]
+                (fH, fW) = face.shape[:2]
+                if fW < 20 or fH < 20:
+                    continue
+
+                faceBlob = cv2.dnn.blobFromImage(
+                    face, 1.0 / 255, (96, 96), (0, 0, 0), swapRB=True, crop=False
+                )
+                embedder.setInput(faceBlob)
+                vec = embedder.forward()
+                preds = model.predict_proba(vec)[0]
+                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
+
+
+iface = gr.Interface(
+    fn=run_inference,
+    inputs="image",
+    outputs="label",
+    live=True,
+    interpretation="default",
+    title="Who do you look Like?!",
+)
+iface.launch()

deploy.prototxt.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f62621cac923d6f37bd669298c428bb7ee72233b5f8c3389bb893e35ebbcf795
+size 28092

openface.nn4.small2.v1.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b72d54aeb24a64a8135dca8e792f7cc675c99a884a6940350a6cedcf7b7ba08
+size 31510785

requirements.txt

@@ -0,0 +1 @@
+

res10_300x300_ssd_iter_140000.caffemodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a56a11a57a4a295956b0660b4a3d76bbdca2206c4961cea8efe7d95c7cb2f2d
+size 10666211

unknownEmbeddings.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef0614d4878acf5106126df50398cee6cf7e90c9e0328c9d44f355c45fbc4c54
+size 5571

unknownNames.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28448313ad59e72fd623d0d4b40cce6a74147f2660aa1f41ba316fc264a1170a
+size 44