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.gitattributes

@@ -0,0 +1,5 @@
+models/charrec.pt filter=lfs diff=lfs merge=lfs -text
+models/large_LP_YOLOm_best.pt filter=lfs diff=lfs merge=lfs -text
+models/yolov8n_lp_det.pt filter=lfs diff=lfs merge=lfs -text
+models/yolov8n_lpchar_det.pt filter=lfs diff=lfs merge=lfs -text
+models/yolov8n.pt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -1,18 +1,26 @@
 import gradio as gr
 import model1 as m1
+import model2 as m2
 
 cars = []
 lps = []
 lp_texts = []
 counter = 0
 
-# this is the main function that passes the images to the model 
-def main(image):    
+# this is the main function that passes the images to the model 1
+def model1(image):    
     global cars, lps, lp_texts, counter
     (cars, lps, lp_texts) = m1.run([image])
     counter = 0
     return cars[0], lps[0], lp_texts[0]
 
+# this is the main function that passes the images to the model 1
+def model2(image):    
+    global lps, lp_texts, counter
+    (lps, lp_texts) = m2.run([image])
+    counter = 0
+    return lps[0], lp_texts[0]
+
 # function to go to next detected car licence plate
 def next_img():
     global counter
@@ -28,6 +36,20 @@ def prev_img():
     return cars[index], lps[index], lp_texts[index]
 
 
+# function to go to next detected licence plate
+def next_img_lp():
+    global counter
+    counter += 1
+    index =  int(counter % len(lps))
+    return lps[index], lp_texts[index]
+
+# function to go to prev detected  licence plate
+def prev_img_lp():
+    global counter
+    counter -= 1
+    index =  int(counter % len(lps))
+    return lps[index], lp_texts[index]
+
 # this code is responcible for the front end part of the page
 with gr.Blocks() as demo:
     gr.Markdown("## ANPR Project")
@@ -46,7 +68,7 @@ with gr.Blocks() as demo:
             next = gr.Button(value="next")
             prev = gr.Button(value="prev")
         
-        submit.click(main, inputs=[img], outputs=[car, lp, lp_text])
+        submit.click(model1, inputs=[img], outputs=[car, lp, lp_text])
         next.click(next_img, outputs=[car, lp, lp_text])
         prev.click(prev_img, outputs=[car, lp, lp_text])
     
@@ -54,5 +76,19 @@ with gr.Blocks() as demo:
         gr.Markdown("Using 2 different ML models")
         gr.Markdown("YOLOv8m for car dection + easy ocr for text detection")
         gr.Markdown("YOLOv8m for car dection is trained on a large dataset of 25K training images")
+        img2 = gr.Image(label="Input")
+        submit2 = gr.Button(value="submit")
+        
+        with gr.Row():       
+            lp2 = gr.Image(label="Licence Plate")
+            lp_text2 = gr.Text(label="Plate Number")    
+            
+        with gr.Row():
+            next2 = gr.Button(value="next")
+            prev2 = gr.Button(value="prev")
+        
+        submit2.click(model2, inputs=[img2], outputs=[lp2, lp_text2])
+        next2.click(next_img_lp, outputs=[lp2, lp_text2])
+        prev2.click(prev_img_lp, outputs=[lp2, lp_text2])
 
 demo.launch(share=False)

model2.py

@@ -0,0 +1,61 @@
+from model1 import reader, np, YOLO
+
+lp_detection = YOLO("models/large_LP_YOLOm_best.pt")
+
+
+# function to detect licence plates in the given car images
+def detect_lp(inputs):
+    lps = []
+    # running the license plate detection model with 50% confidence threshold
+    lp_results = lp_detection.predict(source=inputs, conf=0.5, verbose=False)
+    # iterating through each output (num of outputs will be same as num of inputs)
+    for lp_result in lp_results:
+        # finding the bounding boxes of the license plate detected
+        lp_boxes = lp_result.boxes.xyxy.tolist()    
+        # iterating through each license plate detected
+        for lp_box in lp_boxes:
+            # cropping license plate  image from the car image
+            lp = lp_result.orig_img[int(lp_box[1]):int(lp_box[3]), int(lp_box[0]):int(lp_box[2])]
+            lps.append(lp)
+            # breaking as we only want to detect one licence plate per car
+            break
+        
+        # if no licence plate is detected then we are adding a black image 
+        if len(lp_boxes) == 0:
+            lps.append(np.zeros((100,100,3), np.uint8))
+            
+    return lps
+
+# function to detect licence plate number in the given licence plate images
+def detect_lp_text(inputs):
+    plate_number = []
+    # iterating through each licence plate
+    for input in inputs:
+        # finding the number/text in licence plate
+        result = reader.readtext(input)
+
+        # if no text is found in the licence plate, then adding a default text not found
+        if len(result) == 0:
+            plate_number.append("not found")
+        else:
+            # adding the licence plate number to a list
+            plate_number.append(result[0][1])
+            
+    return plate_number
+
+def run(inputs):
+        
+    # for future, to handle multiple inputs
+    # currently using just one input
+    inputs = inputs[0]
+    
+    # detecting licence plates from the input images
+    # returns licence plate images, if it cant find a license plate a black image is returned
+    lps = detect_lp(inputs)
+    
+    # detecting licence plate number from licence plate images
+    # returns text from the licence plate images, if none is detected "not found" text is returned
+    lp_text = detect_lp_text(lps)
+    
+    return lps, lp_text
+

models/large_LP_YOLOm_best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9600657c811e290bf3c92f0270c8c182d3ff57fa961a5e5bb82e6136ca1cb1b
+size 207513151