Adding passport_MRZ weight

app.py

@@ -14,6 +14,8 @@ import keras_ocr
 import matplotlib.pyplot as plt
 from numpy import asarray
 import pybboxes as pbx
+import pytesseract
+from datetime import date
 
 
 from utils.datasets import LoadStreams, LoadImages
@@ -25,8 +27,8 @@ os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolo
 os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt")
 pipeline = keras_ocr.pipeline.Pipeline()
 
-def detect_Custom(img,boundedImage):
-    model='best' # Naming Convention for yolov7 See output file of https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset/data
+def detect_Custom(img):
+    model='passport_mrz' # Naming Convention for yolov7 See output file of https://www.kaggle.com/code/owaiskhan9654/training-yolov7-on-kaggle-on-custom-dataset/data
     parser = argparse.ArgumentParser()
     parser.add_argument('--weights', nargs='+', type=str, default=model+".pt", help='model.pt path(s)')
     parser.add_argument('--source', type=str, default='Inference/', help='source') 
@@ -130,10 +132,19 @@ def detect_Custom(img,boundedImage):
 
                     if save_img or view_img:
                         label = f'{names[int(cls)]} {conf:.2f}'
+                        if(cls == 1):
+                          x1 = int(xyxy[0].item())
+                          y1 = int(xyxy[1].item())
+                          x2 = int(xyxy[2].item())
+                          y2 = int(xyxy[3].item())
+
+                          orig_img = im0
+                          crop_img = im0[y1:y2, x1:x2]
+                          cv2.imwrite('MRZ_1.png', crop_img)
                         plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
                         W, H = 300, 300
                         box_voc = pbx.convert_bbox(xyxy, from_type="yolo", to_type="voc", image_size=(W,H))
-                        print(box_voc)
+                        
             if view_img:
                 cv2.imshow(str(p), im0)
                 cv2.waitKey(1)  
@@ -157,11 +168,18 @@ def detect_Custom(img,boundedImage):
                     vid_writer.write(im0)
 
     output_text = ''
-    images = [keras_ocr.tools.read(img) for img in [boundedImage]]
-    prediction_groups = pipeline.recognize(images)
-    first=prediction_groups[0]
-    for text,box in first:
-        output_text += ' '+ text
+    text = pytesseract.image_to_string(Image.open('MRZ_1.png'))
+    text = text.replace(" ", "")
+    text=text[22:28]
+    today = date.today()
+    s = today.strftime('%Y%m%d')[2:]
+    if(text > s):
+      output_text = 'This is a Valid Passport'
+    #images = [keras_ocr.tools.read(img) for img in [boundedImage]]
+    #prediction_groups = pipeline.recognize(images)
+    #first=prediction_groups[0]
+    #for text,box in first:
+        #output_text += ' '+ text
     
     if save_txt or save_img:
         s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
@@ -189,4 +207,4 @@ examples1=[["Image1.jpeg", "Yolo_v7_Custom_trained_By_Owais"],["Image2.jpeg", "Y
 Top_Title="<center>Intelligent Image to Text - IIT </center></a>"
 
 css = ".output-image, .input-image, .image-preview {height: 300px !important}"
-gr.Interface(detect_Custom,[gr.Image(type="pil"),gr.Image(type="filepath")],[gr.Image(type="pil"),output],css=css,title=Top_Title,examples=examples1,description=Custom_description,article=Footer,cache_examples=False).launch()
+gr.Interface(detect_Custom,[gr.Image(type="pil")],[gr.Image(type="pil"),output],css=css,title=Top_Title,examples=examples1,description=Custom_description,article=Footer,cache_examples=False).launch()