I'm new to Tensorflow and Python, I'm running custom object detection in Google Colab and following this tutorial -
https://github.com/tensorflow/models/blob/master/docs/vision/object_detection.ipynb
and all the steps were running successfully and got saved_model files. Now I'm running the following code to detect the object through webcam (Flask), but I'm getting the following error: Please help me.
Debugging middleware caught exception in streamed response at a point where response headers were already sent.Traceback (most recent call last): File "C:\ProgramData\Anaconda3\Lib\site-packages\werkzeug\wsgi.py", line 462, in __next__ return self._next() File "C:\ProgramData\Anaconda3\Lib\site-packages\werkzeug\wrappers\response.py", line 50, in _iter_encoded for item in iterable: File "E:\Final project\ran\IP cam\app - Copy (2).py", line 42, in object_detection detections = detect_fn(input_tensor)TypeError: '_UserObject' object is not callablePlease find my complete code below:
from flask import Flask, render_template, Responseimport tensorflow as tfimport numpy as npimport cv2from object_detection.utils import label_map_utilfrom object_detection.utils import visualization_utils as viz_utilsfrom pathlib import Pathfrom PIL import Image, ImageDrawimport ioimport threadingapp = Flask(__name__)detect_fn = tf.saved_model.load("./saved_model/new")PATH_TO_LABELS = './saved_model/new/label_map.pbtxt'with open(PATH_TO_LABELS, 'rb') as f: label_map = f.read()category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)def object_detection(): cap = cv2.VideoCapture(0) while True: success, frame = cap.read() # Read frame from webcam if not success: print("Could not open video device") break # Perform inference input_tensor = tf.convert_to_tensor(frame) input_tensor = input_tensor[tf.newaxis, ...] detections = detect_fn(input_tensor) # Visualize the results viz_utils.visualize_boxes_and_labels_on_image_array( frame, detections['detection_boxes'][0].numpy(), detections['detection_classes'][0].numpy().astype(int), detections['detection_scores'][0].numpy(), category_index, use_normalized_coordinates=True, max_boxes_to_draw=200, min_score_thresh=0.4, agnostic_mode=False) ret, buffer = cv2.imencode('.jpg', frame) frame = buffer.tobytes() yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n'+ frame + b'\r\n') cap.release()@app.route('/')def index(): return render_template('index.html')@app.route('/video_feed')def video_feed(): return Response(object_detection(), mimetype='multipart/x-mixed-replace; boundary=frame')if __name__ == '__main__': app.run(debug=True)