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app_squat.py
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app_squat.py
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import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_dangerously_set_inner_html
import mediapipe as mp
import SquatPosture as sp
from flask import Flask, Response
import cv2
import tensorflow as tf
import numpy as np
from utils import landmarks_list_to_array, label_params, label_final_results
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
model = tf.keras.models.load_model("working_model_1")
class VideoCamera(object):
def __init__(self):
self.video = cv2.VideoCapture(0)
def __del__(self):
self.video.release()
def gen(camera):
cap = camera.video
i=0
with mp_pose.Pose(
min_detection_confidence=0.5,
min_tracking_confidence=0.5) as pose:
while cap.isOpened():
success, image = cap.read()
image = cv2.flip(image, 1)
if not success:
print("Ignoring empty camera frame.")
# If loading a video, use 'break' instead of 'continue'.
# continue
break
image_height, image_width, _ = image.shape
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
dim=(image_width//5, image_height//5)
resized_image = cv2.resize(image, dim)
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
results = pose.process(resized_image)
params = sp.get_params(results)
flat_params = np.reshape(params, (5, 1))
# params will be run through the model
output = model.predict(flat_params.T)
output[0][0] *= 0.7
output[0][1] *= 1.7
output[0][2] *= 4
output[0][3] *= 0
output[0][4] *= 5
output = output * (1 / np.sum(output))
output_name = ['c', 'k', 'h', 'r', 'x', 'i']
output[0][2] += 0.1
# print(output[0][2], output[0][3])
label = ""
for i in range(1, 4):
label += output_name[i] if output[0][i] > 0.5 else ""
if label == "":
label = "c"
label += 'x' if output[0][4] > 0.15 and label=='c' else ''
# print(label, output)
label_final_results(image, label)
i+=1
# mp_drawing.draw_landmarks(
# image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
# coords = landmarks_list_to_array(results.pose_landmarks, image.shape)
# label_params(image, params, coords)
ret, jpeg = cv2.imencode('.jpg', image)
frame = jpeg.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
server = Flask(__name__)
# external_stylesheets = ['./app.css']
app = dash.Dash(__name__, server=server)
app.title = "Posture"
@server.route('/video_feed')
def video_feed():
return Response(gen(VideoCamera()) ,mimetype='multipart/x-mixed-replace; boundary=frame')
app.layout = html.Div(className="main", children=[
html.Link(
rel="stylesheet",
href="/assets/stylesheet.css"
),
dash_dangerously_set_inner_html.DangerouslySetInnerHTML("""
<div class="main-container">
<table cellspacing="20px" class="table">
<tr class="row">
<td> <img src="/assets/animation_for_web.gif" class="logo" /> </td>
</tr>
<tr class="choices">
<td> Your personal AI Gym Trainer </td>
</tr>
<tr class="row">
<td> <img src="/video_feed" class="feed"/> </td>
</tr>
<tr class="disclaimer">
<td> Please ensure that the scene is well lit and your entire body is visible </td>
</tr>
</table>
</div>
"""),
])
if __name__ == '__main__':
app.run_server(debug=True)