Adding image to text code

app.py

@@ -9,6 +9,8 @@ from numpy import random
 from pathlib import Path
 import torch.backends.cudnn as cudnn
 from models.experimental import attempt_load
+import keras_ocr
+import matplotlib.pyplot as plt
 
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
@@ -17,6 +19,7 @@ from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
 os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt")
 os.system("wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt")
+pipeline = keras_ocr.pipeline.Pipeline()
 
 def detect_Custom(img,model,boundedImage):
     if model =='Yolo_v7_Custom_trained_By_Owais':
@@ -146,12 +149,18 @@ def detect_Custom(img,model,boundedImage):
                         vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                     vid_writer.write(im0)
 
+    images = [keras_ocr.tools.read(img) for img in [boundedImage]]
+    prediction_groups = pipeline.recognize(images)
+    plt.figure(figsize = (10,20))
+    
+    fig, ax = plt.subplots(figsize=(10, 20))
+    keras_ocr.tools.drawAnnotations(image=images[0], predictions=prediction_groups[0], ax=ax)
     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 ''
 
     print(f'Done. ({time.time() - t0:.3f}s)')
 
-    return Image.fromarray(im0[:,:,::-1]), boundedImage
+    return Image.fromarray(im0[:,:,::-1]), plt.imshow(boundedImage)