Duplicate from leakyrelu/MobilenetV2SSDLite_LPRnet

Co-authored-by: Carlos Sandoval <[email protected]>

.gitattributes

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README.md

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+---
+title: Automatic Licence plate Recognition
+emoji: 🦀
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 2.9.4
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: leakyrelu/MobilenetV2SSDLite_LPRnet
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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+import gradio as gr
+import re, datetime,time, cv2, numpy as np, tensorflow as tf, sys
+
+
+CHARS = "ABCDEFGHIJKLMNPQRSTUVWXYZ0123456789" # exclude I, O
+CHARS_DICT = {char:i for i, char in enumerate(CHARS)}
+DECODE_DICT = {i:char for i, char in enumerate(CHARS)}
+
+interpreter = tf.lite.Interpreter(model_path='detection.tflite')
+interpreter.allocate_tensors()
+recog_interpreter = tf.lite.Interpreter(model_path='recognition2.tflite')
+recog_input_details = recog_interpreter.get_input_details()
+recog_output_details = recog_interpreter.get_output_details()
+recog_interpreter.resize_tensor_input(recog_input_details[0]['index'], (1, 24, 94, 3))
+recog_interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+    
+
+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
+    print(frame.shape)
+    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,cv2.resize(save_frame,(94,24))
+
+def greet(image):
+    resized = cv2.resize(image, (320,320), 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, crop = execute_text_recognition_tflite(
+                boxes[0][i], image,
+                recog_interpreter, recog_input_details, recog_output_details,
+            )
+            return text, crop
+image = gr.inputs.Image(shape=(1920,1080))
+output_image =gr.outputs.Image(type="auto", label="Output")
+
+
+title = "Automatic licence plate detection and recognition"
+description = "Gradio demo for an automatic licence plate recognition system. To use it, simply upload your image of a car with a licence plate, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://ieeexplore.ieee.org/document/9071863'>Robust Real time Lightweight Automatic License plate Recognition System for Iranian License Plates</a> | <a href='https://github.com/clsandoval/LPRnet-keras'>Github Repo</a></p>"
+
+
+iface = gr.Interface(
+    fn=greet,
+    inputs=image,
+    outputs=["text",output_image],
+    title = title,
+    description = description,
+    article=article,
+    examples = [
+        "3.jpg",
+        "4.jpg",
+    ]
+    )
+iface.launch()

detection.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9a985cc86131fac5be60478f4c10be416dfe035445b70813d6441ced7d330018
+size 11495036

recognition.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b080d01c1c84eaa207c8ca5834070bd76ce8d62fe6a4dce7c31d238462a07796
+size 820132

recognition2.tflite

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e5552d69c86c8d596be83aad9dffdf9ee466a5932db2f37d07bbe37cab64e35
+size 797248

requirements.txt

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+opencv-contrib-python==4.5.4.58
+opencv-python==4.5.5.62
+opencv-python-headless==4.5.4.58
+tensorflow==2.7.0