Update app.py

app.py

@@ -1,194 +1,3 @@
-# import logging
-# 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.turn import get_ice_servers
-# from cvzone.HandTrackingModule import HandDetector
-# from cvzone.SelfiSegmentationModule import SelfiSegmentation
-# import time
-# import os
-
-# logger = logging.getLogger(__name__)
-
-# st.title("Interactive Virtual Keyboard with Twilio Integration")
-# st.info("Use your webcam to interact with the virtual keyboard via hand gestures.")
-
-# class Button:
-#     def __init__(self, pos, text, size=[100, 100]):
-#         self.pos = pos
-#         self.size = size
-#         self.text = text
-
-# # Initialize components
-# detector = HandDetector(maxHands=1, detectionCon=0.8)
-# # segmentor = SelfiSegmentation()
-# # keys = [["Q", "W", "E", "R", "T", "Y", "U", "I", "O", "P"],
-# #         ["A", "S", "D", "F", "G", "H", "J", "K", "L", ";"],
-# #         ["Z", "X", "C", "V", "B", "N", "M", ",", ".", "/"]]
-
-# # listImg = os.listdir('model/street')
-# # imgList = [cv2.imread(f'model/street/{imgPath}') for imgPath in listImg]
-# # indexImg = 0
-
-
-# # # Function to process the video frame from the webcam
-# # def process_video_frame(frame, detector, segmentor, imgList, indexImg, keys, session_state):
-# #     # Convert the frame to a numpy array (BGR format)
-# #     image = frame.to_ndarray(format="bgr24")
-    
-# #     # Remove background using SelfiSegmentation
-# #     imgOut = segmentor.removeBG(image, imgList[indexImg])
-
-# #     # Detect hands on the background-removed image
-# #     hands, img = detector.findHands(imgOut, flipType=False)
-    
-# #     # Create a blank canvas for the keyboard
-# #     keyboard_canvas = np.zeros_like(img)
-# #     buttonList = []
-
-# #     # Create buttons for the virtual keyboard based on the keys list
-# #     for key in keys[0]:
-# #         buttonList.append(Button([30 + keys[0].index(key) * 105, 30], key))
-# #     for key in keys[1]:
-# #         buttonList.append(Button([30 + keys[1].index(key) * 105, 150], key))
-# #     for key in keys[2]:
-# #         buttonList.append(Button([30 + keys[2].index(key) * 105, 260], key))
-
-# #     # Draw the buttons on the keyboard canvas
-# #     for button in buttonList:
-# #         x, y = button.pos
-# #         cv2.rectangle(keyboard_canvas, (x, y), (x + button.size[0], y + button.size[1]), (255, 255, 255), -1)
-# #         cv2.putText(keyboard_canvas, button.text, (x + 20, y + 70), cv2.FONT_HERSHEY_PLAIN, 5, (0, 0, 0), 3)
-
-# #     # Handle input and gestures from detected hands
-# #     if hands:
-# #         for hand in hands:
-# #             lmList = hand["lmList"]
-# #             if lmList:
-# #                 # Get the coordinates of the index finger tip (landmark 8)
-# #                 x8, y8 = lmList[8][0], lmList[8][1]
-# #                 for button in buttonList:
-# #                     bx, by = button.pos
-# #                     bw, bh = button.size
-# #                     # Check if the index finger is over a button
-# #                     if bx < x8 < bx + bw and by < y8 < by + bh:
-# #                         # Highlight the button and update the text
-# #                         cv2.rectangle(img, (bx, by), (bx + bw, by + bh), (0, 255, 0), -1)
-# #                         cv2.putText(img, button.text, (bx + 20, by + 70), cv2.FONT_HERSHEY_PLAIN, 5, (255, 255, 255), 3)
-# #                         # Update the output text in session_state
-# #                         session_state["output_text"] += button.text
-
-# #     # Corrected return: Create a video frame from the ndarray image
-# #     return av.VideoFrame.from_ndarray(img, format="bgr24")
-
-
-
-
-
-
-# # Shared state for output text
-# if "output_text" not in st.session_state:
-#     st.session_state["output_text"] = ""
-
-# class Detection(NamedTuple):
-#     label: str
-#     score: float
-#     box: np.ndarray
-
-
-# @st.cache_resource  # Cache label colors
-# def generate_label_colors():
-#     return np.random.uniform(0, 255, size=(2, 3))  # Two classes: Left and Right Hand
-
-
-# COLORS = generate_label_colors()
-
-# # Initialize MediaPipe Hands
-# mp_hands = mp.solutions.hands
-# detector = mp_hands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5)
-
-# # Session-specific caching
-# result_queue: "queue.Queue[List[Detection]]" = queue.Queue()
-
-# # Hand detection callback
-# def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-#     image = frame.to_ndarray(format="bgr24")
-#     h, w = image.shape[:2]
-
-#     # Process image with MediaPipe Hands
-#     results = detector.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
-
-#     detections = []
-#     if results.multi_hand_landmarks:
-#         for hand_landmarks, hand_class in zip(results.multi_hand_landmarks, results.multi_handedness):
-#             # Extract bounding box
-#             x_min, y_min = 1, 1
-#             x_max, y_max = 0, 0
-#             for lm in hand_landmarks.landmark:
-#                 x_min = min(x_min, lm.x)
-#                 y_min = min(y_min, lm.y)
-#                 x_max = max(x_max, lm.x)
-#                 y_max = max(y_max, lm.y)
-
-#             # Scale bbox to image size
-#             box = np.array([x_min * w, y_min * h, x_max * w, y_max * h]).astype("int")
-
-#             # Label and score
-#             label = hand_class.classification[0].label
-#             score = hand_class.classification[0].score
-
-#             detections.append(Detection(label=label, score=score, box=box))
-
-#             # Draw bounding box and label
-#             color = COLORS[0 if label == "Left" else 1]
-#             cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), color, 2)
-#             caption = f"{label}: {round(score * 100, 2)}%"
-#             cv2.putText(
-#                 image,
-#                 caption,
-#                 (box[0], box[1] - 15 if box[1] - 15 > 15 else box[1] + 15),
-#                 cv2.FONT_HERSHEY_SIMPLEX,
-#                 0.5,
-#                 color,
-#                 2,
-#             )
-
-#     # Put results in the queue
-#     result_queue.put(detections)
-
-#     return av.VideoFrame.from_ndarray(image, format="bgr24")
-
-
-
-# webrtc_ctx = webrtc_streamer(
-#     key="keyboard-demo",
-#     mode=WebRtcMode.SENDRECV,
-#     rtc_configuration={
-#         "iceServers": get_ice_servers(),
-#         "iceTransportPolicy": "relay",
-#     },
-#     video_frame_callback=video_frame_callback,
-#     media_stream_constraints={"video": True, "audio": False},
-#     async_processing=True,
-# )
-
-
-# st.markdown("### Instructions")
-# st.write(
-#     """
-#     1. Turn on your webcam using the checkbox above.
-#     2. Use hand gestures to interact with the virtual keyboard.
-#     """
-# ) 
-
-#)
-
 import logging
 import queue
 from pathlib import Path
@@ -295,45 +104,45 @@ def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
 
 
             
-            # # 1. Bend Forward Warning
-            # 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)
+            # 1. Bend Forward Warning
+            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)
 
-            # # 2. Lean Backward Warning
-            # 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)
+            # 2. Lean Backward Warning
+            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
+            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'
+            # 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'