add app.py

age_deploy.prototxt

@@ -0,0 +1,175 @@
+name: "CaffeNet"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 227
+input_dim: 227
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 96
+    kernel_size: 7
+    stride: 4
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "conv1"
+  top: "conv1"
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm1"
+  type: LRN
+  bottom: "pool1"
+  top: "norm1"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv2"
+  type: CONVOLUTION
+  bottom: "norm1"
+  top: "conv2"
+  convolution_param {
+    num_output: 256
+    pad: 2
+    kernel_size: 5
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "conv2"
+  top: "conv2"
+}
+layers {
+  name: "pool2"
+  type: POOLING
+  bottom: "conv2"
+  top: "pool2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "norm2"
+  type: LRN
+  bottom: "pool2"
+  top: "norm2"
+  lrn_param {
+    local_size: 5
+    alpha: 0.0001
+    beta: 0.75
+  }
+}
+layers {
+  name: "conv3"
+  type: CONVOLUTION
+  bottom: "norm2"
+  top: "conv3"
+  convolution_param {
+    num_output: 384
+    pad: 1
+    kernel_size: 3
+  }
+}
+layers{
+  name: "relu3" 
+  type: RELU
+  bottom: "conv3"
+  top: "conv3"
+}
+layers {
+  name: "pool5"
+  type: POOLING
+  bottom: "conv3"
+  top: "pool5"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+layers {
+  name: "fc6"
+  type: INNER_PRODUCT
+  bottom: "pool5"
+  top: "fc6"
+  inner_product_param {
+    num_output: 512
+  }
+}
+layers {
+  name: "relu6"
+  type: RELU
+  bottom: "fc6"
+  top: "fc6"
+}
+layers {
+  name: "drop6"
+  type: DROPOUT
+  bottom: "fc6"
+  top: "fc6"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc7"
+  type: INNER_PRODUCT
+  bottom: "fc6"
+  top: "fc7"
+  inner_product_param {
+    num_output: 512
+  }
+}
+layers {
+  name: "relu7"
+  type: RELU
+  bottom: "fc7"
+  top: "fc7"
+}
+layers {
+  name: "drop7"
+  type: DROPOUT
+  bottom: "fc7"
+  top: "fc7"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layers {
+  name: "fc8"
+  type: INNER_PRODUCT
+  bottom: "fc7"
+  top: "fc8"
+  inner_product_param {
+    num_output: 8
+  }
+}
+layers {
+  name: "prob"
+  type: SOFTMAX
+  bottom: "fc8"
+  top: "prob"
+}

app.py

@@ -0,0 +1,72 @@
+import gradio as gr
+import cv2
+import dlib
+
+def age_predict(photo):
+
+    # photo = "face_age/001/16.png"
+
+    img = cv2.imread(photo)
+    img = cv2.resize(img, (720, 640))
+    frame = img.copy()
+
+    # Model for Age detection
+    age_weights = "age_deploy.prototxt"
+    age_config = "age_net.caffemodel"
+    age_Net = cv2.dnn.readNet(age_config, age_weights)
+
+    # Model requirements for image
+    ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
+    model_mean = (78.4263377603, 87.7689143744, 114.895847746) # Taken from the official site
+
+    # Storing the image dimensions
+    fH = img.shape[0]
+    fW = img.shape[1]
+
+    Boxes = [] # to store the face co-ordinates
+    mssg = 'Face Detected'
+
+    # Model for face detection
+    face_detector = dlib.get_frontal_face_detector()
+    # converting to grayscale
+    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detecting the faces
+    faces = face_detector(img_gray)
+
+    # If no faces are detected
+    if not faces:
+        mssg = 'No face detected'
+        # cv2.putText(img, f'{mssg}', (40,40), cv2.FONT_HERSHEY_SIMPLEX, 2, (200), 2)
+        # cv2.imshow('image',img)
+        # cv2.waitKey(0)
+        return mssg
+    else:
+        # Bounding face
+        for face in faces:
+            x = face.left() # extracting the face coordinates
+            y = face.top()
+            x2 = face.right()
+            y2 = face.bottom()
+
+            # Rescaling those coordinates for our image
+            box = [x, y, x2, y2]
+            Boxes.append(box)
+            cv2.rectangle(frame, (x,y), (x2, y2), (00, 200, 200), 2)
+
+        for box in Boxes:
+            face = frame[box[1]:box[3], box[0]:box[2]]
+            
+            # Image preprocessing
+            blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), model_mean, swapRB = False)
+            
+            # Age Prediction
+            age_Net.setInput(blob)
+            age_preds = age_Net.forward()
+            age = ageList[age_preds[0].argmax()]
+            
+            # cv2.putText(frame, f'{mssg}:{age}', (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255),2,cv2.LINE_AA)
+            
+            # cv2.imshow('image', frame)
+            # cv2.waitKey(0)
+            return f'{mssg}:{age}'