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import os
import base64
from flask import Flask, render_template,request
import config
#newly added
from flask_cors import CORS, cors_origin
#local imports
from detection_model_run import run_detection
from helper import preprocess_keypoints
from classification_model_run import run_classification
from generate_light import generate_new_image
from show_points import display_keypoints
app = Flask(__name__)
app.config.from_object(config)
UPLOAD_FOLDER = 'captures' # Define the directory to save uploaded images
# Ensure the upload directory exists
if not os.path.exists(UPLOAD_FOLDER):
os.makedirs(UPLOAD_FOLDER)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
@app.route('/')
def index():
print("hello")
return render_template("index.html")
def run_model_evaluation(image_path, useGan=False, imageID=0):
# run keypoint detection on image from camera
if useGan:
new_image_path = generate_new_image(image_path, app.config['GAN_MODEL_WEIGHTS_PATH'], imageID = imageID)
keypoints= run_detection(new_image_path, app.config['POSE_MODEL_WEIGHTS_PATH_GAN'])
if isinstance(keypoints, str):
display_keypoints(keypoints, ganImage = True, imageID = imageID)
return 'No keypoints detected'
display_keypoints(keypoints, ganImage = True, imageID = imageID)
else:
keypoints = run_detection(image_path, app.config['POSE_MODEL_WEIGHTS_PATH_NOGAN'])
if isinstance(keypoints, str):
display_keypoints(keypoints, ganImage = False, imageID = imageID)
return 'No keypoints detected'
display_keypoints(keypoints, ganImage = False, imageID = imageID)
#preprocess the keypoints for classification
input_array = preprocess_keypoints(keypoints)
predicted_class = run_classification(input_array)
categoryOrder = ['basketball', 'bowling', 'boxing', 'football', 'golf', 'hacky sack',
'rowing, stationary', 'skateboarding', 'skiing, downhill', 'soccer',
'softball, general',
'tennis, hitting balls, non-game play, moderate effort']
return categoryOrder[predicted_class]
@app.route('/upload', methods=['POST'])
def upload():
data_url = request.json.get('image_data')
useGAN = request.json.get('use_model_gan')
imageID = request.json.get('unique_ID')
if data_url:
print("starting")
# Remove header from base64 encoded image
img_data = data_url.split(',')[1]
# Save the image to a file
image_path = os.path.join(app.config['UPLOAD_FOLDER'], f'image_{imageID}.png')
with open( image_path,'wb') as f:
f.write(base64.b64decode(img_data))
print("hello",image_path)
if useGAN == False:
answer = run_model_evaluation(image_path, useGan = False, imageID = imageID)
else:
answer = run_model_evaluation(image_path, useGan = True, imageID= imageID)
return answer
return 'No image data received.'
if __name__=="__main__":
app.run()
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