New Output Field

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 *.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+*.ttf filter=lfs diff=lfs merge=lfs -text

Util/Fonts/kalpurush.ttf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83613a03e53aad8337f2be9fb974409a23c1d99b3b4b494567ba9d156c492b01
+size 314592

app.py

@@ -62,7 +62,10 @@ import cv2
 # from myverify import verify
 #from detect_frame import detect_frame
 # import pathlib
-
+HF_TOKEN = os.getenv('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')
+hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "ocr_flag")
+from PIL import ImageFont, ImageDraw, Image
+font = ImageFont.truetype("Util\Fonts\kalpurush.ttf", 35)
 
 #import more
 import tensorflow as tf
@@ -528,7 +531,31 @@ def model_predict(word):
     #     os.makedirs(folderName)
     # fileName = folderName+ "/" + time_now + ".png"
     # cv2.imwrite(fileName,word)
-    return output
+    pil_image = Image.fromarray(word)
+        #pil_image.convert("RGBA")
+    for i in range(0,15):
+        if mark[i]==0:
+            continue
+        x0=(detections['detection_boxes'][i][0])*row
+        y0=(detections['detection_boxes'][i][1])*col
+        x1=(detections['detection_boxes'][i][2])*row
+        y1=(detections['detection_boxes'][i][3])*col
+        pt1 = (y0,x0)
+        pt2 = (y1,x1)
+        # color = (0, 0, 255)  # Red color in BGR format
+        # thickness = 2  # Border thickness in pixels
+        # word = cv2.rectangle(word, pt1, pt2, color, thickness)
+        draw = ImageDraw.Draw(pil_image,"RGBA")
+        curi=detections['detection_classes'][i]
+        classi=classes[curi]
+        shape = [(y0,x0), (y1, x1)]
+        draw.rectangle(shape,fill=(0, 100, 200, 127))
+        draw.rectangle(shape, outline=(0, 0, 0, 127), width=3)
+        bbox = draw.textbbox(pt1, classi, font=font)
+        draw.rectangle(bbox, fill=(200, 100, 0, 200))
+        draw.text(pt1, classi, font=font, fill=(0,0,0,255))
+    newWordImg = np.asarray(pil_image)
+    return newWordImg, output
     '''
     output=''
     for i in range(0,len(final)):
@@ -547,6 +574,6 @@ def model_predict(word):
 import gradio as gr
 
 
-demo = gr.Interface(fn=model_predict, inputs= "paint", outputs="text")
+demo = gr.Interface(fn=model_predict, inputs= "paint", outputs=["image","text"],allow_flagging="auto",flagging_callback=hf_writer)
 
-demo.launch() 
+demo.launch()