Update app.py

app.py

@@ -42,19 +42,21 @@ def calculate_angle(a, b, c):
         angle = 360 - angle
     return angle
 
-# counterL=0#Counter checks for number of curls
-# correct=0
-# incorrect=0
-# stage='mid'#it checks if we our hand is UP or DOWN
+counterL=0#Counter checks for number of curls
+correct=0
+incorrect=0
 
 # Detection Queue
 result_queue: "queue.Queue[List[Detection]]" = queue.Queue()
 
 def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-    counterL=0#Counter checks for number of curls
-    correct=0
-    incorrect=0
-    stage='mid'#it checks if we our hand is UP or DOWN
+    global counterL, correct, incorrect, stage #The change made
+        # Initialize stage if not defined
+    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)
@@ -92,40 +94,40 @@ def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
             
             #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)
-            
-            # # Squat logic
-            # if 80 < angleKneeL < 110 and 29 < angleHipL < 40:
-            #     cv2.putText(image, "Squat Detected!", (300, 100), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
-            # else:
-            #     if angleHipL < 29:
-            #         cv2.putText(image, "Lean Forward!", (300, 200), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
-            #     elif angleHipL > 45:
-            #         cv2.putText(image, "Lean Backward!", (300, 200), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
-            #     if angleKneeL < 80:
-            #         cv2.putText(image, "Squat Too Deep!", (300, 250), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
-            #     elif angleKneeL > 110:
-            #         cv2.putText(image, "Lower Your Hips!", (300, 300), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
 
 
-            
-            # 1. Bend Forward Warning
+
+
+           # 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)
-
-            # 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)
+
+            # # # 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 
@@ -142,11 +144,13 @@ def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
             #    incorrect +=1
             #    stage='up'
 
-            # stage 4 
-            if (80 < angleKneeL < 110):
-               # if (18 < angleHipL < 40):  # Valid "down" position
-               correct+=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'
 
 
             
@@ -193,224 +197,3 @@ webrtc_streamer(
     video_frame_callback=video_frame_callback,
     async_processing=True,
 )
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-# import logging
-# import cv2
-# import numpy as np
-# import streamlit as st
-# from streamlit_webrtc import WebRtcMode, webrtc_streamer
-# from cvzone.HandTrackingModule import HandDetector
-# from cvzone.SelfiSegmentationModule import SelfiSegmentation
-# import os
-# import time
-# import av
-# import queue
-# from typing import List, NamedTuple
-# from sample_utils.turn import get_ice_servers
-
-# logger = logging.getLogger(__name__)
-
-# # Streamlit settings
-# st.set_page_config(page_title="Virtual Keyboard", layout="wide")
-# st.title("Interactive Virtual Keyboard")
-# st.subheader('''Turn on the webcam and use hand gestures to interact with the virtual keyboard.
-# Use 'a' and 'd' from the keyboard to change the background.''')
-
-# # Initialize modules
-# detector = HandDetector(maxHands=1, detectionCon=0.8)
-# segmentor = SelfiSegmentation()
-
-# # Define virtual keyboard layout
-# 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", ",", ".", "/"]]
-
-# class Button:
-#     def __init__(self, pos, text, size=[100, 100]):
-#         self.pos = pos
-#         self.size = size
-#         self.text = text
-
-# class Detection(NamedTuple):
-#     label: str
-#     score: float
-#     box: np.ndarray
-
-# # result_queue: "queue.Queue[List[Detection]]" = queue.Queue()
-
-# listImg = os.listdir('model/street') if os.path.exists('model/street') else []
-# if not listImg:
-#     st.error("Error: 'street' directory is missing or empty. Please add background images.")
-#     st.stop()
-# else:
-#     imgList = [cv2.imread(f'model/street/{imgPath}') for imgPath in listImg if cv2.imread(f'model/street/{imgPath}') is not None]
-
-# indexImg = 0
-# prev_key_time = [time.time()] * 2
-# output_text = ""
-
-# if "output_text" not in st.session_state:
-#     st.session_state["output_text"] = ""
-
-
-# # def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-# #     img = frame.to_ndarray(format="bgr24")
-# #     hands, img = detector.findHands(img, flipType=False)
-
-# #     # Render hand detection results
-
-# #     if hands:
-# #         hand = hands[0]
-# #         bbox = hand["bbox"]
-# #         cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), (255, 0, 0), 2)
-
-# #         cv2.putText(img, 'OpenCV', (50,50), font, 
-# #                    fontScale, color, thickness, cv2.LINE_AA)
-# #         cv2.putText(img, 'OpenCV', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 255, 255), 1, cv2.LINE_AA)
-
-# #         result_queue.put(hands)
-        
-# #     return av.VideoFrame.from_ndarray(img, format="bgr24")
-
-
-# result_queue: "queue.Queue[List[Detection]]" = queue.Queue()
-
-
-# def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-#     image = frame.to_ndarray(format="bgr24")
-
-#     # Run inference
-#     blob = cv2.dnn.blobFromImage(
-#         cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5
-#     )
-#     net.setInput(blob)
-#     output = net.forward()
-
-#     h, w = image.shape[:2]
-
-#     # Convert the output array into a structured form.
-#     output = output.squeeze()  # (1, 1, N, 7) -> (N, 7)
-#     output = output[output[:, 2] >= score_threshold]
-#     detections = [
-#         Detection(
-#             class_id=int(detection[1]),
-#             label=CLASSES[int(detection[1])],
-#             score=float(detection[2]),
-#             box=(detection[3:7] * np.array([w, h, w, h])),
-#         )
-#         for detection in output
-#     ]
-
-#     # Render bounding boxes and captions
-#     for detection in detections:
-#         caption = f"{detection.label}: {round(detection.score * 100, 2)}%"
-#         color = COLORS[detection.class_id]
-#         xmin, ymin, xmax, ymax = detection.box.astype("int")
-
-#         cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
-#         cv2.putText(
-#             image,
-#             caption,
-#             (xmin, ymin - 15 if ymin - 15 > 15 else ymin + 15),
-#             cv2.FONT_HERSHEY_SIMPLEX,
-#             0.5,
-#             color,
-#             2,
-#         )
-
-#     result_queue.put(detections)
-
-#     return av.VideoFrame.from_ndarray(image, format="bgr24")
-
-
-# # def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-# #     global indexImg, output_text
-
-# #     img = frame.to_ndarray(format="bgr24")
-# #     imgOut = segmentor.removeBG(img, imgList[indexImg])
-# #     hands, imgOut = detector.findHands(imgOut, flipType=False)
-
-# #     buttonList = [Button([30 + col * 105, 30 + row * 120], key) for row, line in enumerate(keys) for col, key in enumerate(line)]
-
-# #     detections = []
-# #     if hands:
-# #         for i, hand in enumerate(hands):
-# #             lmList = hand['lmList']
-# #             bbox = hand['bbox']
-# #             label = "Hand"
-# #             score = hand['score']
-# #             box = np.array([bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3]])
-# #             detections.append(Detection(label=label, score=score, box=box))
-
-# #             if lmList:
-# #                 x4, y4 = lmList[4][0], lmList[4][1]
-# #                 x8, y8 = lmList[8][0], lmList[8][1]
-# #                 distance = np.sqrt((x8 - x4) ** 2 + (y8 - y4) ** 2)
-# #                 click_threshold = 10
-
-# #                 for button in buttonList:
-# #                     x, y = button.pos
-# #                     w, h = button.size
-# #                     if x < x8 < x + w and y < y8 < y + h:
-# #                         cv2.rectangle(imgOut, button.pos, (x + w, y + h), (0, 255, 160), -1)
-# #                         cv2.putText(imgOut, button.text, (x + 20, y + 70), cv2.FONT_HERSHEY_PLAIN, 5, (255, 255, 255), 3)
-
-# #                         if (distance / np.sqrt(bbox[2] ** 2 + bbox[3] ** 2)) * 100 < click_threshold:
-# #                             if time.time() - prev_key_time[i] > 2:
-# #                                 prev_key_time[i] = time.time()
-# #                                 if button.text != 'BS' and button.text != 'SPACE':
-# #                                     output_text += button.text
-# #                                 elif button.text == 'BS':
-# #                                     output_text = output_text[:-1]
-# #                                 else:
-# #                                     output_text += ' '
-
-# #     result_queue.put(detections)
-# #     st.session_state["output_text"] = output_text
-# #     return av.VideoFrame.from_ndarray(imgOut, format="bgr24")
-
-    
-
-# webrtc_streamer(
-#     key="virtual-keyboard",
-#     mode=WebRtcMode.SENDRECV,
-#     rtc_configuration={"iceServers": get_ice_servers(), "iceTransportPolicy": "relay"},
-#     media_stream_constraints={"video": True, "audio": False},
-#     video_frame_callback=video_frame_callback,
-#     async_processing=True,
-# )
-
-# st.subheader("Output Text")
-# st.text_area("Live Input:", value=st.session_state["output_text"], height=200)
-
-