add initial test

app.py

@@ -1,8 +1,69 @@
 import gradio as gr
+import re, datetime,time, cv2, numpy as np, tensorflow as tf, sys
 
+interpreter = tf.lite.Interpreter(model_path='detection.tflite')
+interpreter.allocate_tensors()
+recog_interpreter = tf.lite.Interpreter(model_path='recognition.tflite')
+recog_interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+recog_input_details = recog_interpreter.get_input_details()
+recog_output_details = recog_interpreter.get_output_details()
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+def execute_text_recognition_tflite( boxes, frame, interpreter, input_details, output_details):
+    x1, x2, y1, y2 = boxes[1], boxes[3], boxes[0], boxes[2]
+    save_frame = frame[
+        max( 0, int(y1*1079) ) : min( 1079, int(y2*1079) ),
+        max( 0, int(x1*1920) ) : min( 1920, int(x2*1920) )
+    ]
+
+    # Execute text recognition
+
+    test_image = cv2.resize(save_frame,(94,24))/256
+    test_image = np.expand_dims(test_image,axis=0)
+    test_image = test_image.astype(np.float32)
+    interpreter.set_tensor(input_details[0]['index'], test_image)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    decoded = tf.keras.backend.ctc_decode(output_data,(24,),greedy=False)
+    text = ""
+    for i in np.array(decoded[0][0][0]):
+        if i >-1:
+            text += DECODE_DICT[i]
+    # Do nothing if text is empty
+    if not len(text): return 
+    license_plate = text
+    text[:3].replace("0",'O')
+
+    return text
+
+def greet(image):
+    resized = cv2.resize(image, (320,320), interpolation=cv2.INTER_AREA)
+    demo_frame = cv2.resize(image, (680,480), interpolation=cv2.INTER_AREA)
+    input_data = resized.astype(np.float32)          # Set as 3D RGB float array
+    input_data /= 255.                               # Normalize
+    input_data = np.expand_dims(input_data, axis=0)  # Batch dimension (wrap in 4D)
+
+    # Initialize input tensor
+    interpreter.set_tensor(input_details[0]['index'], input_data)
+    interpreter.invoke()
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+
+    # Bounding boxes
+    boxes = interpreter.get_tensor(output_details[1]['index'])
+
+    text = None
+    # For index and confidence value of the first class [0]
+    for i, confidence in enumerate(output_data[0]):
+        if confidence > .3:
+            text = execute_text_recognition_tflite(
+                boxes[0][i], image,
+                recog_interpreter, recog_input_details, recog_output_details,
+            )
+            return text
+image = gr.inputs.Image(shape=(320,320))
+
+iface = gr.Interface(fn=greet, inputs=image, outputs="text")
 iface.launch()

detection.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a985cc86131fac5be60478f4c10be416dfe035445b70813d6441ced7d330018
+size 11495036

recognition.tflite

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b080d01c1c84eaa207c8ca5834070bd76ce8d62fe6a4dce7c31d238462a07796
+size 820132