Update app.py

app.py

@@ -4,36 +4,30 @@ import streamlit as st
 from PIL import Image
 from streamlit_webrtc import VideoProcessorBase, webrtc_streamer
 
-# Update these paths based on where you saved the models
+# Paths to the models
 AGE_PROTOTXT_PATH = 'deploy_age.prototxt'
 AGE_MODEL_PATH = 'age_net.caffemodel'
 GENDER_PROTOTXT_PATH = 'deploy_gender.prototxt'
 GENDER_MODEL_PATH = 'gender_net.caffemodel'
 
-# Load pre-trained models for age and gender detection
+# Load the models
 age_net = cv2.dnn.readNetFromCaffe(AGE_PROTOTXT_PATH, AGE_MODEL_PATH)
 gender_net = cv2.dnn.readNetFromCaffe(GENDER_PROTOTXT_PATH, GENDER_MODEL_PATH)
 
-# Define age and gender lists
+# Lists for age and gender
 AGE_LIST = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
 GENDER_LIST = ['Male', 'Female']
 
 MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
 
 def analyze_frame(frame):
-    # Convert frame to blob
     blob = cv2.dnn.blobFromImage(frame, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
-    
-    # Predict gender
     gender_net.setInput(blob)
     gender_preds = gender_net.forward()
     gender = GENDER_LIST[gender_preds[0].argmax()]
-    
-    # Predict age
     age_net.setInput(blob)
     age_preds = age_net.forward()
     age = AGE_LIST[age_preds[0].argmax()]
-    
     return gender, age
 
 def detect_shirt_color(frame, face_coords):
@@ -41,51 +35,38 @@ def detect_shirt_color(frame, face_coords):
     shirt_region = frame[y + h:y + h + int(h / 2), x:x + w]
     avg_color_per_row = np.average(shirt_region, axis=0)
     avg_color = np.average(avg_color_per_row, axis=0)
-    
-    if np.all(avg_color > [200, 200, 200]):  # Simplified check for white
+    if np.all(avg_color > [200, 200, 200]):
         return 'white'
-    elif np.all(avg_color < [50, 50, 50]):  # Simplified check for black
+    elif np.all(avg_color < [50, 50, 50]):
         return 'black'
     else:
         return 'other'
 
 def process_frame(frame):
-    # Convert frame to grayscale
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    # Detect faces
     face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
     faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-    
     males = 0
     females = 0
-    
     for (x, y, w, h) in faces:
         face = frame[y:y+h, x:x+w]
         gender, age = analyze_frame(face)
-        
         shirt_color = detect_shirt_color(frame, (x, y, w, h))
         if shirt_color == 'white':
             age = '23'
         elif shirt_color == 'black':
             age = 'Child'
-        
         if len(faces) < 2:
-            # Skip shirt color logic if there are less than 2 people
             gender, age = analyze_frame(face)
-
         if gender == 'Male':
             males += 1
         else:
             females += 1
-        
         label_text = f'{gender}, {age}'
         cv2.putText(frame, label_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
         cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-    
-    # Display the number of males and females
     info_text = f'Males: {males}, Females: {females}'
     cv2.putText(frame, info_text, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-    
     return frame
 
 class VideoProcessor(VideoProcessorBase):