Update app.py

app.py

@@ -3,13 +3,11 @@ import queue
 from pathlib import Path
 from typing import List, NamedTuple
 import mediapipe as mp
-
 import av
 import cv2
 import numpy as np
 import streamlit as st
 from streamlit_webrtc import WebRtcMode, webrtc_streamer
-
 from sample_utils.download import download_file
 from sample_utils.turn import get_ice_servers
 
@@ -17,8 +15,34 @@ from sample_utils.turn import get_ice_servers
 logger = logging.getLogger(__name__)
 
 # Streamlit setup
-st.title("AI Squat Detection using WebRTC")
-st.info("Use your webcam for real-time squat detection.")
+st.set_page_config(page_title="AI Squat Detection", page_icon="🏋️")
+st.markdown(
+    """<style>
+    .status-box {
+        background: #f7f7f7;
+        padding: 15px;
+        border-radius: 8px;
+        box-shadow: 2px 2px 5px rgba(0,0,0,0.1);
+        margin-bottom: 20px;
+        font-size: 18px;
+    }
+    .title {
+        color: #2E86C1;
+        font-size: 32px;
+        font-weight: bold;
+        text-align: center;
+        margin-bottom: 10px;
+    }
+    .info {
+        text-align: center;
+        font-size: 18px;
+        margin-bottom: 20px;
+        color: #333;
+    }
+    </style>""", unsafe_allow_html=True)
+
+st.markdown('<div class="title">AI Squat Detection</div>', unsafe_allow_html=True)
+st.markdown('<div class="info">Use your webcam for real-time squat detection.</div>', unsafe_allow_html=True)
 
 # Initialize MediaPipe components
 mp_pose = mp.solutions.pose
@@ -30,7 +54,6 @@ class Detection(NamedTuple):
     score: float
     box: np.ndarray
 
-
 # Angle calculation function
 def calculate_angle(a, b, c):
     a = np.array(a)
@@ -42,152 +65,73 @@ def calculate_angle(a, b, c):
         angle = 360 - angle
     return angle
 
-counterL=0#Counter checks for number of curls
-correct=0
-incorrect=0
-
 # Detection Queue
 result_queue: "queue.Queue[List[Detection]]" = queue.Queue()
 
+# Initialize MediaPipe Pose once
+pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
+
 def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-    global counterL, correct, incorrect, stage #The change made
-        # Initialize stage if not defined
+    global counterL, correct, incorrect, stage
     if 'stage' not in globals():
         stage = 'up'
         correct = 0
         incorrect = 0
-        
+
     image = frame.to_ndarray(format="bgr24")
     h, w = image.shape[:2]
     image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-    with mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
-        results = pose.process(image_rgb)
-        landmarks = results.pose_landmarks.landmark if results.pose_landmarks else []
-
-        # Corrected detection logic
-        detections = [
-            Detection(
-                class_id=0,  # Assuming a generic class_id for pose detections
-                label="Pose",
-                score=0.7,  # Full confidence as pose landmarks were detected
-                box=np.array([0, 0, image.shape[1], image.shape[0]])  # Full image as bounding box
-            )
-        ] if landmarks else []
-
-        if landmarks:
-            hipL = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, 
-                   landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
-            kneeL = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, 
-                    landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
-            ankleL = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x, 
-                     landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
-            shoulderL = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, 
-                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
-            footIndexL = [landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x, 
-                    landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y]
-
-            # Calculate angles
-            angleKneeL = calculate_angle(hipL, kneeL, ankleL)
-            angleHipL = calculate_angle(shoulderL, hipL, [hipL[0], 0])
-            angleAnkleL = calculate_angle(footIndexL, ankleL, kneeL)
-            
-            #Visualize of left leg
-            cv2.putText(image, str(angleHipL),tuple(np.multiply(angleHipL, [640, 480]).astype(int)),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2, cv2.LINE_AA)
-
-
-
-
-           # Update squat stage and count correct reps
-            if angleKneeL > 110 and stage == 'down':
-                stage = 'up'
-                if 18 < angleHipL < 40:
-                    correct += 1
-
-            if 80 < angleKneeL < 110 and stage == 'up':
-                stage = 'down'
-
-            # Display feedback messages
-            if 10 < angleHipL < 18:
-               cv2.rectangle(image, (310, 180), (450, 220), (0, 0, 0), -1) 
-               cv2.putText(image,f"Bend Forward",(320,200),cv2.FONT_HERSHEY_SIMPLEX,1,(150,120,255),1,cv2.LINE_AA)
-                
-            if angleHipL > 45:
-               cv2.rectangle(image, (310, 180), (450, 220), (0, 0, 0), -1)
-               cv2.putText(image,f"Bend Backward",(320,200),cv2.FONT_HERSHEY_SIMPLEX,1,(80,120,255),1,cv2.LINE_AA)
-            
-
-
-
-
-            # # # stage 2
-
-            # # # Incorrect movements
-
-            # # 3. Knees not low enough
-            # if 110 < angleKneeL < 130:
-            #    cv2.rectangle(image, (220, 40), (450, 80), (0, 0, 0), -1)
-            #    cv2.putText(image,f"Lower Your Hips",(230,60),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),1,cv2.LINE_AA)
-       
-        
-            # # 3. Knees not low enough and not completed the squat 
-            # if angleKneeL>130 and stage=='mid':
-            #    cv2.rectangle(image, (220, 40), (450, 80), (0, 0, 0), -1)
-            #    cv2.putText(image,f"Lower Your Hips",(230,60),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),1,cv2.LINE_AA)
-            #    incorrect+=1
-            #    stage='up'
-
-            # # 4. Squat too deep
-            # if angleKneeL < 80 and stage=='mid':
-            #    cv2.rectangle(image, (220, 40), (450, 80), (0, 0, 0), -1)
-            #    cv2.putText(image,f"Squat too deep",(230,60),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),1,cv2.LINE_AA)
-            #    incorrect +=1
-            #    stage='up'
-
-            # # stage 4 
-            # if (80 < angleKneeL < 110) and stage=='mid':
-            #    if (18 < angleHipL < 40):  # Valid "down" position
-            #       correct+=1
-            #       stage='up'
-            # if (angleKneeL>110):
-            #     stage='mid'
-
-
-            
-        # cv2.putText(image,f"Correct:{correct}",
-        #        (400,120),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,0),1,cv2.LINE_AA)
-        # cv2.putText(image,f"Incorrect:{incorrect}",
-        #        (400,150),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,0),1,cv2.LINE_AA)
-
-    #Render Counter  to our camera screen
-    #Setup Status box
-        cv2.rectangle(image,(0,0),(500,80),(245,117,16),-1)
-    
-    #REP data
-    
-        cv2.putText(image,'Left',(10,12),
-               cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1,cv2.LINE_AA)
-    
-        cv2.putText(image,str(correct),
-               (10,60),cv2.FONT_HERSHEY_SIMPLEX,2,(255,255,255),2,cv2.LINE_AA)
-    
-    #Stage data for left leg
-    
-        cv2.putText(image,'STAGE',(230,12),
-               cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),1,cv2.LINE_AA)
-    
-        cv2.putText(image,stage,
-               (230,60),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),1,cv2.LINE_AA)
-            
-
-        mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,mp_drawing.DrawingSpec(color=(255, 175, 0), thickness=2, circle_radius=2),mp_drawing.DrawingSpec(color=(0, 255, 200), thickness=2, circle_radius=2))
+    results = pose.process(image_rgb)
+    landmarks = results.pose_landmarks.landmark if results.pose_landmarks else []
 
-    result_queue.put(detections)
-    return av.VideoFrame.from_ndarray(image, format="bgr24")
+    detections = [
+        Detection(
+            class_id=0, label="Pose", score=0.5, box=np.array([0, 0, w, h])
+        )
+    ] if landmarks else []
+
+    if landmarks:
+        hipL = [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x, landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y]
+        kneeL = [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y]
+        ankleL = [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x, landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
+        shoulderL = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
 
+        angleKneeL = calculate_angle(hipL, kneeL, ankleL)
+        angleHipL = calculate_angle(shoulderL, hipL, [hipL[0], 0])
 
+        rel_point1 = (int(w * 0), int(h - h * 0.55))
+        rel_point2 = (int(w * 0.265625), int(h - h * 0.55))
 
-# WebRTC streamer configuration
+        cv2.rectangle(image, (0, 110), (280, 225), (127, 248, 236), -1)
+        cv2.rectangle(image, (0, 113), (277, 222), (12, 85, 61), -1)
+        cv2.putText(image, str(int(angleHipL)), rel_point1, cv2.FONT_HERSHEY_SIMPLEX, 1.7, (255, 255, 255), 2, cv2.LINE_AA)
+        cv2.putText(image, str(int(angleKneeL)), rel_point2, cv2.FONT_HERSHEY_SIMPLEX, 1.7, (255, 255, 255), 2, cv2.LINE_AA)
+
+        if angleKneeL > 110 and stage == 'down':
+            stage = 'up'
+            if 18 < angleHipL < 40:
+                correct += 1
+
+        if 80 < angleKneeL < 110 and stage == 'up':
+            stage = 'down'
+
+    cv2.rectangle(image, (0, 0), (280, 103), (127, 248, 236), -1)
+    cv2.rectangle(image, (0, 3), (277, 100), (12, 85, 61), -1)
+
+    cv2.putText(image, 'Left', (10, 22), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2, cv2.LINE_AA)
+    cv2.putText(image, str(correct), (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.7, (255, 255, 255), 2, cv2.LINE_AA)
+    cv2.putText(image, 'STAGE', (180, 22), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2, cv2.LINE_AA)
+    cv2.putText(image, stage, (147, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.7, (255, 255, 255), 2, cv2.LINE_AA)
+
+    mp_drawing.draw_landmarks(
+        image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS,
+        mp_drawing.DrawingSpec(color=(255, 175, 0), thickness=2, circle_radius=2),
+        mp_drawing.DrawingSpec(color=(0, 255, 200), thickness=2, circle_radius=2)
+    )
+
+    result_queue.put(detections)
+    return av.VideoFrame.from_ndarray(image, format="bgr24")
 
 webrtc_streamer(
     key="squat-detection",
@@ -196,4 +140,4 @@ webrtc_streamer(
     media_stream_constraints={"video": True, "audio": False},
     video_frame_callback=video_frame_callback,
     async_processing=True,
-)
+)