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app.py

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+# %%
+# pip install torch torchvision torchaudio yolov5
+import gradio as gr
+import cv2
+import requests
+import os
+import torch 
+import numpy as np
+from ultralytics import YOLO
+import yolov5
+import easyocr
+from paddleocr import PaddleOCR
+import keras_ocr
+import matplotlib.pyplot as plt
+from paddleocr import draw_ocr
+import pandas as pd
+
+# %%
+# Loading Yolo V5 model
+model = yolov5.load('License_Plate_Model_Y5.pt')#, device="cpu")
+
+# %%
+def get_LicencePlate(frame, conf_threshold: gr.inputs.Slider = 0.1):
+    
+    lst_plate_xyxy = []
+    # Setting model configuration 
+    model.conf = conf_threshold
+
+    results = model(frame)
+    for index, row in results.pandas().xyxy[0].iterrows():
+        xmin = int(row['xmin'])
+        ymin = int(row['ymin'])
+        xmax = int(row['xmax'])
+        ymax = int(row['ymax'])
+        class_name=(row['name'])
+        if (class_name == 'license-plate'):
+            lst_plate_xyxy.append((xmin,ymin,xmax,ymax, class_name))
+
+    return lst_plate_xyxy
+
+# %%
+def extract_License_Number_PaddleOCR(image_path, conf_threshold):
+    image = cv2.imread(image_path)
+    # image = cv2.resize(image,(1020,800))
+    results = get_LicencePlate(image, conf_threshold)
+    PaddleOCR_output = []
+    # words = []
+    # boxes = []
+    # scores = []
+    for index in results:
+        x1 = index[0]
+        y1 = index[1]
+        x2 = index[2]
+        y2 = index[3]
+        class_name=(index[4])
+
+        cropped_img = image[y1:y2, x1:x2]
+
+        # processed_img = cropped_img
+        processed_img =  cropped_img
+
+        # Perform OCR using PaddleOCR
+        paddle_ocr = PaddleOCR()
+        paddle_ocr_result = paddle_ocr.ocr(processed_img)
+
+        PaddleOCR_output.append(paddle_ocr_result)    
+
+        print('\nPaddleOCR:')
+        print(''.join([text[1][0] + ' ' for text in paddle_ocr_result[0]]))
+
+        # Extract the words and bounding boxes from the OCR results
+        words = []
+        boxes = []
+        scores = []
+        for line in paddle_ocr_result:
+            for bbox in line:
+                words.append(bbox[1][0])
+                scores.append(bbox[1][1])
+                boxes.append(bbox[0])
+        
+        output_image = cv2.rectangle(image,(x1,y1),(x2,y2),(0,0,255),2)
+        cv2.putText(output_image,str(words),(x1,y1),cv2.FONT_HERSHEY_PLAIN,2,(255,0,255),3)
+
+    return words, boxes, scores, output_image    
+
+# %%
+def extract_License_Number_EasyOCR(image_path,conf_threshold ):
+    image = cv2.imread(image_path)
+    # image = cv2.resize(image,(1020,800))
+    results = get_LicencePlate(image ,conf_threshold=0.1  )
+    easyOCR_output = []
+    paddle_output = []
+
+    # print("results************************", results)
+    for index in results:
+        x1 = index[0]
+        y1 = index[1]
+        x2 = index[2]
+        y2 = index[3]
+        class_name=(index[4])
+
+        cropped_img = image[y1:y2, x1:x2]
+        processed_img =  cropped_img
+
+        easyocr_reader = easyocr.Reader(['en'], verbose=False)
+        easyocr_result = easyocr_reader.readtext(processed_img)
+
+        easyOCR_output.append(easyocr_result)     
+
+        print('\nEasyOCR:')
+        print(''.join([text[1] + ' ' for text in easyocr_result]))
+
+        # Extract the words and bounding boxes from the OCR results
+        words = []
+        boxes = []
+        scores = []
+        for line in easyocr_result:
+            words.append(line[1])
+            scores.append(line[2])
+            boxes.append(line[0])
+        output_image = cv2.rectangle(image,(x1,y1),(x2,y2),(0,0,255),2)
+        cv2.putText(output_image,str(words),(x1,y1),cv2.FONT_HERSHEY_PLAIN,1,(255,0,255),2)
+  
+    return words, boxes, scores, output_image
+
+# %%
+def createDataframe(words, boxes, scores):
+    df = pd.DataFrame(list(zip(words, boxes, scores)), columns=['words', 'boxes', 'scores'])
+    return df
+
+# %%
+def initiate_Extract(image_path, conf_threshold: gr.inputs.Slider = 0.10, ocr_type="PaddleOCR"):
+
+    if ocr_type == "PaddleOCR":
+        words, boxes, scores, output_img = extract_License_Number_PaddleOCR(image_path, conf_threshold)
+    elif ocr_type == "EasyOCR":
+        words, boxes, scores, output_img = extract_License_Number_EasyOCR(image_path, conf_threshold)
+    else:
+        words, boxes, scores, output_img = extract_License_Number_PaddleOCR(image_path ,conf_threshold )
+
+    dataframe = createDataframe(words, boxes, scores)
+    return cv2.cvtColor(output_img, cv2.COLOR_BGR2RGB), dataframe
+
+
+# %%
+import numpy as np
+title = "License Plate Number Recognition using YOLO & Paddle OCR - EasyOCR"
+description = ""
+
+css = """.output_image, .input_image {height: 600px !important}"""
+examples = [['truck1.jpg'],['c1.jpg']]
+iface = gr.Interface(fn=initiate_Extract,
+                     inputs=[
+                        gr.inputs.Image(type="filepath", label="Input Image"),
+                        gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.10, step=0.05, label="Confidence Threshold"),
+                        gr.inputs.Dropdown(label="Select the OCR",default="PaddleOCR", choices=["PaddleOCR", "EasyOCR"]),
+                     ],
+                     outputs=[gr.outputs.Image(type="pil", label="annotated image"),"dataframe"] ,
+                     title=title,
+                     description=description,
+                     examples=examples,
+                     css=css,
+                     analytics_enabled = True, enable_queue=True)
+
+iface.launch(inline=False , debug=True)
+
+# %%
+
+
+# %%
+
+
+

requirements.txt

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+gradio==3.4.0
+opencv-python
+numpy<1.24
+ultralytics
+yolov5
+paddleocr
+easyocr
+torch