new examples and face detection algo

app.py

@@ -1,21 +1,66 @@
 import gradio as gr
 import numpy as np
 import urllib
+import cv2
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.models import load_model
 
+# Load the pre-trained face detection model
+face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+
 # Load the pre-trained model
 model = load_model('my_model.h5')
 def classify_image(img):
- # Preprocess the input image
- img = image.img_to_array(img)
- img = np.expand_dims(img, axis=0)
- img /= 255.0
+ #print("image : ",type(img))
+ #image_array = img_to_array(image)
+ #print("image shape: ",img.shape)
+ img_copy = img
+ height, width = img_copy.shape[0], img_copy.shape[1]
+
+ img_copy = cv2.resize(img_copy, (500, 500))
+ # Convert the image to grayscale
+ gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+
+ # Detect faces in the image
+ faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+ # Check if any faces were detected
+ if len(faces) > 0:
+ #print("Human face detected in the image!")
+ face_area_list = []
+ # Draw rectangles around the detected faces
+ for (x, y, w, h) in faces:
+ cv2.rectangle(img_copy, (x, y), (x+w, y+h), (0, 255, 0), 2)
+ area = w * h
+ face_area_list.append(area)
+ #print(sorted(face_area_list))
+ big_face_area = sorted(face_area_list)[-1]
+ img_area = img_copy.shape[0] * img_copy.shape[1]
+ #print(big_face_area)
+ #print(img_area)
+ #print("% Area of the largest face in image:", (big_face_area/img_area)*100)
+ perc_area = (big_face_area/img_area)*100
+ if perc_area>7.5:
+ img = image.img_to_array(img)
+ img = np.expand_dims(img, axis=0)
+ img /= 255.0
+ 
+ # Use the model to make a prediction
+ prediction = model.predict(img)[0]
+ # Map the predicted class to a label
+ dic = {'SFW': float(prediction[1]), 'NSFW': float(prediction[0])}
+ else :
+ dic = {'SFW': float(0), 'NSFW': float(1)}
+ # Display the image with the detected faces
+ #cv2_imshow(img)
+ #cv2.imshow('Detected faces', img_copy)
+ #cv2.waitKey(0)
+ #cv2.destroyAllWindows()
+ # Preprocess the input image
+ 
+ else:
+ dic = {'SFW': float(0), 'NSFW': float(1)}
+ #print("No human face detected in the image.")
  
- # Use the model to make a prediction
- prediction = model.predict(img)[0]
- # Map the predicted class to a label
- dic = {'SFW': float(prediction[1]), 'NSFW': float(prediction[0])}
  return dic
 
 def classify_url(url):
@@ -27,7 +72,7 @@ def classify_url(url):
 
 
 # Define the GRADIO output interface
-examples = [f"example{i}.jpg" for i in range(2,5)]
+examples = [f"example{i}.jpg" for i in range(1,9)]
 # Define the GRADIO app
 app = gr.Interface(classify_image, gr.Image(shape=(224, 224)), outputs="label", allow_flagging="never", examples = examples,title="NSFW/SFW Classifier")