v4

app.py

@@ -1,7 +1,59 @@
 import gradio as gr
+import cv2
+import pickle
+import skimage
+from skimage.feature import local_binary_pattern
 
-def greet(name):
-    return "Hello " + name + "!!"
+clf = None
+with open('classifier.pkl', 'rb') as f:
+    clf = pickle.load(f)
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+def img2text(img):
+  # print(img)
+  # Resize the image to a specific width and height
+  image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+  resized_image = cv2.resize(image, (24, 24))
+
+  # Compute the LBP feature vector
+  lbp_feature_vector = local_binary_pattern(resized_image, 8, 1, method="uniform")
+
+  # Print the feature vector
+  # print(lbp_feature_vector)
+
+  flattened_arr = lbp_feature_vector.reshape(-1)
+  # print(flattened_arr)
+
+  y_pred = clf.predict([flattened_arr])
+  return str(y_pred[0])
+
+
+import gradio as gr 
+
+# gr.Interface(txt2img, gr.Image(), gr.Text(), title = 'Stable Diffusion 2.0 Colab with Gradio UI').launch(share = True, debug = True)
+
+iface = gr.Interface(img2text, gr.Image(), gr.Text(), title = 'Face Detector: Local Binary Pattern method, Support Vector Machine algorithm')
 iface.launch()
+
+
+
+
+# file_path = 'images/Copy of 35.jpg'
+
+# # Load the image
+# image = cv2.imread(file_path)
+# print(image.shape)
+
+# # Resize the image to a specific width and height
+# image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+# resized_image = cv2.resize(image, (24, 24))
+
+# lbp_feature_vector = local_binary_pattern(resized_image, 8, 1, method="uniform")
+
+# flattened_arr = lbp_feature_vector.reshape(-1)
+
+
+# y_pred = clf.predict([flattened_arr])
+# print(y_pred)

classifier.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8734e11d6f73c9d24a3a36b74c5a797cf3a8d559f2c91650158cb5cd18a07123
+size 575021