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app.py

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+import cv2
+import numpy as np
+import time
+#import os  
+#import datetime
+#from datetime import datetime
+#from PIL import Image
+#from io import BytesIO
+#import requests
+#from scipy import ndimage
+
+
+INPUT_WIDTH = 320
+INPUT_HEIGHT = 320
+SCORE_THRESHOLD = 0.45
+NMS_THRESHOLD = 0.45
+CONFIDENCE_THRESHOLD = 0.5
+
+# Text parameters.
+FONT_FACE = cv2.FONT_HERSHEY_SIMPLEX
+FONT_SCALE = 0.7
+THICKNESS = 1
+
+# Colors.
+BLACK  = (0,0,0)
+BLUE   = (255,178,50)
+YELLOW = (0,255,255)
+classesFile = "coco.names"
+classes = None
+
+
+ch_detection_modelWeights = "best_upwork.onnx"
+ch_detection_model = cv2.dnn.readNet(ch_detection_modelWeights)
+x_=["-","0","1","2","3","4","5","6","7","8","9"]
+
+
+
+
+
+def draw_label(im, label, x, y):
+    """Draw text onto image at location."""
+    # Get text size.
+    text_size = cv2.getTextSize(label, FONT_FACE, FONT_SCALE, THICKNESS)
+    dim, baseline = text_size[0], text_size[1]
+    # Use text size to create a BLACK rectangle.
+    cv2.rectangle(im, (x,y), (x + dim[0], y + dim[1] + baseline), (0,0,0), cv2.FILLED);
+    # Display text inside the rectangle.
+    cv2.putText(im, label, (x, y + dim[1]), FONT_FACE, FONT_SCALE, YELLOW, THICKNESS, cv2.LINE_AA)
+def pre_process(input_image, net,w,h):
+      # Create a 4D blob from a frame.
+      #print(input_image.shape)
+      blob = cv2.dnn.blobFromImage(input_image, 1/255,  (w, h), [0,0,0], 1, crop=False)
+
+      # Sets the input to the network.
+      net.setInput(blob)
+
+      # Run the forward pass to get output of the output layers.
+      outputs = net.forward(net.getUnconnectedOutLayersNames())
+      return outputs
+
+def get_xyxy(input_image,image_height,image_width, outputs,w,h):
+      # Lists to hold respective values while unwrapping.
+      class_ids = []
+      confidences = []
+      boxes = []
+      output_boxes=[]
+      results_cls_id=[]
+      # Rows.
+      rows = outputs[0].shape[1]
+
+      x_factor = image_width / w
+      y_factor =  image_height / h
+      # Iterate through detections.
+      for r in range(rows):
+            row = outputs[0][0][r]
+            confidence = row[4]
+            # Discard bad detections and continue.
+            if confidence >= CONFIDENCE_THRESHOLD:
+                  classes_scores = row[5:]
+                  # Get the index of max class score.
+                  class_id = np.argmax(classes_scores)
+                  #  Continue if the class score is above threshold.
+                  if (classes_scores[class_id] > SCORE_THRESHOLD):
+                        confidences.append(confidence)
+                        class_ids.append(class_id)
+                        cx, cy, w, h = row[0], row[1], row[2], row[3]
+                        left = int((cx - w/2) * x_factor)
+                        top = int((cy - h/2) * y_factor)
+                        width = int(w * x_factor)
+                        height = int(h * y_factor)
+                        box = np.array([left, top, width, height,])
+                        boxes.append(box)
+ # Perform non maximum suppression to eliminate redundant, overlapping boxes with lower confidences.
+      indices = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE_THRESHOLD, NMS_THRESHOLD)
+      for i in indices:
+            box = boxes[i]
+            left = box[0]
+            top = box[1]
+            width = box[2]
+            height = box[3] 
+            results_cls_id.append(class_ids[i])
+         
+            cv2.rectangle(input_image, (left, top), (left + width, top + height), BLUE, 1)
+
+            boxes[i][2]=left + width
+            boxes[i][3]=top + height
+            #check if the height is suitable
+            output_boxes.append(boxes[i])
+      cv2.imwrite('x1.jpg',input_image)
+      return output_boxes,results_cls_id #boxes (left,top,width,height)
+
+def char_det(input_image,ch_detection_model,w,h):
+      #in_image_copy=input_image.copy()
+      detections = pre_process(input_image.copy(), ch_detection_model,w,h) #detection results 
+      image_height=input_image.shape[0]
+      image_width=input_image.shape[1]
+      bounding_boxes=get_xyxy(input_image,image_height,image_width, detections,w,h)
+      #date = datetime.now().strftime("%Y_%m_%d_%I_%M_%S_%p")
+      #im_name=f"ch_{date}.jpg"
+      #print(im_name)
+      #cv2.imwrite(im_name,image_with_bounding_boxes)
+     # cv2.imwrite('x1.jpg',image_with_bounding_boxes)
+      return bounding_boxes
+
+def rearange_(array_pred,results_cls_id):
+      scores=''
+      #print(y2,y2[:,0])
+      ind=np.argsort(array_pred[:,0])
+
+      #print(license_image.shape[0],ind)
+      for indx in (ind):
+        scores=scores+x_[results_cls_id[indx]]
+        
+
+      return scores
+
+
+def main_func(img,):
+      scores=0
+      t1=time.time()
+      img = np.array(img) 
+      im2=img.copy()
+      
+      #send_im_2_tg(img)
+      #cv2.imwrite(f"inp.jpg",img)
+      width_height_diff=img.shape[1]-img.shape[0] #padding
+      #print(width_height_diff,img.shape)
+      if width_height_diff>0:
+            img = cv2.copyMakeBorder(img, 0, width_height_diff, 0, 0, cv2.BORDER_CONSTANT, (0,0,0))
+      if width_height_diff<0:
+            img = cv2.copyMakeBorder(img, 0, 0, 0, int(-1*width_height_diff), cv2.BORDER_CONSTANT, (0,0,0))
+
+    
+      cropped_chars_array,results_cls_id=char_det(img.copy(),ch_detection_model,320,320)
+      if len(cropped_chars_array)!=0:
+                     cropped_chars_array=np.asarray(cropped_chars_array)
+                     scores=rearange_(cropped_chars_array,results_cls_id)
+                     for box in cropped_chars_array:
+                            left,top,width,height=box
+                            cv2.rectangle(im2, (left, top), (width,height), BLUE, 1)
+      
+      time_of_process=(time.time()-t1)
+      
+      #return scores,time_of_process
+      return scores,im2,time_of_process
+
+
+import gradio as gr
+def final_func():
+	gr.Interface(fn=main_func, 
+             inputs=gr.Image(),
+             outputs=[gr.Textbox(lines=1, label="Scores"),gr.Image(label="Image"),gr.Number(label="Time")]).launch()
+	
+if __name__ == "__main__":
+    final_func()

best_upwork.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9909e56eef03c83f2851a799db30444e623af3f843d87bad8d113664f707813
+size 28317318