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Screening Backend/app__1_.py

+import glob
+import cv2 as cv
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from werkzeug.utils import secure_filename
+from flask import Flask, request, jsonify
+from flask_cors import CORS 
+
+app = Flask(__name__)
+CORS(app)
+
+model = tf.keras.models.load_model('sign_detector.h5')
+model.compile(
+            optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), 
+            loss='categorical_crossentropy', 
+            metrics=[
+                tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
+                tf.keras.metrics.Precision(name='precision'),
+                tf.keras.metrics.Recall(name='recall')
+            ])
+
+class_dict = {
+            '1': 0,
+            '3': 1,
+            '4': 2,
+            '5': 3,
+            '7': 4,
+            '8': 5,
+            '9': 6,
+            'A': 7,
+            'B': 8,
+            'Baby': 9,
+            'Brother': 10,
+            'C': 11,
+            'D': 12,
+            'Dont_like': 13,
+            'E': 14,
+            'F': 15,
+            'Friend': 16,
+            'G': 17,
+            'H': 18,
+            'Help': 19,
+            'House': 20,
+            'I': 21,
+            'J': 22,
+            'K': 23,
+            'L': 24,
+            'Like': 25,
+            'Love': 26,
+            'M': 27,
+            'Make': 28,
+            'More': 29,
+            'N': 30,
+            'Name': 31,
+            'No': 32,
+            'O_OR_0': 33,
+            'P': 34,
+            'Pay': 35,
+            'Play': 36,
+            'Q': 37,
+            'R': 38,
+            'S': 39,
+            'Stop': 40,
+            'T': 41,
+            'U': 42,
+            'V_OR_2': 43,
+            'W_OR_6': 44,
+            'With': 45,
+            'X': 46,
+            'Y': 47,
+            'Yes': 48,
+            'Z': 49,
+            'nothing': 50
+            }
+class_dict_rev = {v: k for k, v in class_dict.items()}
+
+# test_dataset_dir = 'data/test'
+# train_dataset_dir = 'data/train'
+# validation_dataset_dir = 'data/validation'
+
+# all_images = glob.glob(f"{test_dataset_dir}/*/*.*") +\
+#              glob.glob(f"{train_dataset_dir}/*/*.*") +\
+#              glob.glob(f"{validation_dataset_dir}/*/*.*")
+# all_images = list(set([img.replace('\\', '/') for img in all_images]))
+# file_names = [path.split('/')[-1] for path in all_images]
+# class_names = [path.split('/')[-2] for path in all_images]
+# file_dict = {file_name : class_name for file_name,class_name in zip(file_names, class_names)}
+
+
+def inference(
+            image_path,
+            target_size=(224, 224)
+            ):
+    image_path = image_path.replace('\\', '/')
+    img = cv.imread(image_path)
+    img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+    img = cv.resize(img, target_size)
+    img = np.expand_dims(img, axis=0)
+    img = tf.keras.applications.xception.preprocess_input(img)
+    prediction = model.predict(img).squeeze()
+    p = prediction.argmax()
+    proba = prediction[p]
+    proba = round(proba, 3)
+    return class_dict_rev[int(p)], proba
+    
+@app.route('/sign', methods=['POST'])
+def sign():
+    image_obj = request.files['image_path']
+    sign_obj = request.form['sign']
+    filename = image_obj.filename
+
+    image_path = f'uploads/{filename}'
+    image_obj.save(image_path)
+
+    prediction, proba = inference(image_path)
+    if prediction == sign_obj:
+        matched = True
+    else:
+        matched = False
+    return jsonify({
+                    "PredSignType": f"{prediction}",
+                    "TrueSignType": f"{sign_obj}",
+                    "probability" : f"{proba}",
+                    "matched": f"{matched}"
+                    })
+
+if __name__ == '__main__':
+    app.run(debug=True)
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