Updated to match main's final version

Object_detection_picamera.py

+######## Picamera Object Detection Using Tensorflow Classifier #########
+#
+# Author: Evan Juras
+# Date: 4/15/18
+# Description: 
+# This program uses a TensorFlow classifier to perform object detection.
+# It loads the classifier uses it to perform object detection on a Picamera feed.
+# It draws boxes and scores around the objects of interest in each frame from
+# the Picamera. It also can be used with a webcam by adding "--usbcam"
+# when executing this script from the terminal.
+
+## Some of the code is copied from Google's example at
+## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
+
+## and some is copied from Dat Tran's example at
+## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py
+
+## but I changed it to make it more understandable to me.
+
+
+# Import packages
+import os
+import utils
+import cv2
+import numpy as np
+from picamera.array import PiRGBArray
+from picamera import PiCamera
+import tensorflow as tf
+import argparse
+import sys
+
+# Set up camera constants
+IM_WIDTH = 1280
+IM_HEIGHT = 720
+#IM_WIDTH = 640    Use smaller resolution for
+#IM_HEIGHT = 480   slightly faster framerate
+
+# Select camera type (if user enters --usbcam when calling this script,
+# a USB webcam will be used)
+camera_type = 'picamera'
+parser = argparse.ArgumentParser()
+parser.add_argument('--usbcam', help='Use a USB webcam instead of picamera',
+                    action='store_true')
+args = parser.parse_args()
+if args.usbcam:
+    camera_type = 'usb'
+
+# This is needed since the working directory is the object_detection folder.
+sys.path.append('..')
+
+# Import utilites
+from utils import label_map_util
+from utils import visualization_utils as vis_util
+
+# Name of the directory containing the object detection module we're using
+MODEL_NAME = 'ssdlite_mobilenet_v2_coco_2018_05_09'
+
+# Grab path to current working directory
+CWD_PATH = os.getcwd()
+
+# Path to frozen detection graph .pb file, which contains the model that is used
+# for object detection.
+PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
+
+# Path to label map file
+PATH_TO_LABELS = os.path.join(CWD_PATH,'data','mscoco_label_map.pbtxt')
+
+# Number of classes the object detector can identify
+NUM_CLASSES = 90
+
+## Load the label map.
+# Label maps map indices to category names, so that when the convolution
+# network predicts `5`, we know that this corresponds to `airplane`.
+# Here we use internal utility functions, but anything that returns a
+# dictionary mapping integers to appropriate string labels would be fine
+label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
+categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
+category_index = label_map_util.create_category_index(categories)
+
+# Load the Tensorflow model into memory.
+detection_graph = tf.Graph()
+with detection_graph.as_default():
+    od_graph_def = tf.GraphDef()
+    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
+        serialized_graph = fid.read()
+        od_graph_def.ParseFromString(serialized_graph)
+        tf.import_graph_def(od_graph_def, name='')
+
+    sess = tf.Session(graph=detection_graph)
+
+
+# Define input and output tensors (i.e. data) for the object detection classifier
+
+# Input tensor is the image
+image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+
+# Output tensors are the detection boxes, scores, and classes
+# Each box represents a part of the image where a particular object was detected
+detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+
+# Each score represents level of confidence for each of the objects.
+# The score is shown on the result image, together with the class label.
+detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
+detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
+
+# Number of objects detected
+num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+
+# Initialize frame rate calculation
+frame_rate_calc = 1
+freq = cv2.getTickFrequency()
+font = cv2.FONT_HERSHEY_SIMPLEX
+
+# Initialize camera and perform object detection.
+# The camera has to be set up and used differently depending on if it's a
+# Picamera or USB webcam.
+
+# I know this is ugly, but I basically copy+pasted the code for the object
+# detection loop twice, and made one work for Picamera and the other work
+# for USB.
+
+### Picamera ###
+if camera_type == 'picamera':
+    # Initialize Picamera and grab reference to the raw capture
+    camera = PiCamera()
+    camera.resolution = (IM_WIDTH,IM_HEIGHT)
+    camera.framerate = 10
+    rawCapture = PiRGBArray(camera, size=(IM_WIDTH,IM_HEIGHT))
+    rawCapture.truncate(0)
+
+    for frame1 in camera.capture_continuous(rawCapture, format="bgr",use_video_port=True):
+
+        t1 = cv2.getTickCount()
+        
+        # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
+        # i.e. a single-column array, where each item in the column has the pixel RGB value
+        frame = np.copy(frame1.array)
+        frame.setflags(write=1)
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame_expanded = np.expand_dims(frame_rgb, axis=0)
+
+        # Perform the actual detection by running the model with the image as input
+        (boxes, scores, classes, num) = sess.run(
+            [detection_boxes, detection_scores, detection_classes, num_detections],
+            feed_dict={image_tensor: frame_expanded})
+
+        # Draw the results of the detection (aka 'visulaize the results')
+        
+        """
+        Changes made to pull detected objects
+        """
+        top_score = np.squeeze(scores)[0]
+        top_class = int(np.squeeze(classes)[0])
+        
+        if top_score > 0.4:
+            class_name = category_index[top_class]['name']
+            print(f"Detected: {class_name} (confidence: {top_score:.2f})")
+            
+            with open("/home/pi/detected_object.txt","w") as f:
+                f.write(class_name)
+                
+            camera.close()
+            cv2.destroyAllWindows()
+            break
+        
+        
+        vis_util.visualize_boxes_and_labels_on_image_array(
+            frame,
+            np.squeeze(boxes),
+            np.squeeze(classes).astype(np.int32),
+            np.squeeze(scores),
+            category_index,
+            use_normalized_coordinates=True,
+            line_thickness=8,
+            min_score_thresh=0.40)
+
+        cv2.putText(frame,"FPS: {0:.2f}".format(frame_rate_calc),(30,50),font,1,(255,255,0),2,cv2.LINE_AA)
+
+        # All the results have been drawn on the frame, so it's time to display it.
+        cv2.imshow('Object detector', frame)
+
+        t2 = cv2.getTickCount()
+        time1 = (t2-t1)/freq
+        frame_rate_calc = 1/time1
+
+        # Press 'q' to quit
+        if cv2.waitKey(1) == ord('q'):
+            break
+
+        rawCapture.truncate(0)
+
+    # removed so that the camera is shut after detection
+    camera.close()
+
+### USB webcam ###
+elif camera_type == 'usb':
+    # Initialize USB webcam feed
+    camera = cv2.VideoCapture(0)
+    ret = camera.set(3,IM_WIDTH)
+    ret = camera.set(4,IM_HEIGHT)
+
+    while(True):
+
+        t1 = cv2.getTickCount()
+
+        # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
+        # i.e. a single-column array, where each item in the column has the pixel RGB value
+        ret, frame = camera.read()
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame_expanded = np.expand_dims(frame_rgb, axis=0)
+
+        # Perform the actual detection by running the model with the image as input
+        (boxes, scores, classes, num) = sess.run(
+            [detection_boxes, detection_scores, detection_classes, num_detections],
+            feed_dict={image_tensor: frame_expanded})
+
+        # Draw the results of the detection (aka 'visulaize the results')
+        vis_util.visualize_boxes_and_labels_on_image_array(
+            frame,
+            np.squeeze(boxes),
+            np.squeeze(classes).astype(np.int32),
+            np.squeeze(scores),
+            category_index,
+            use_normalized_coordinates=True,
+            line_thickness=8,
+            min_score_thresh=0.85)
+
+        cv2.putText(frame,"FPS: {0:.2f}".format(frame_rate_calc),(30,50),font,1,(255,255,0),2,cv2.LINE_AA)
+        
+        # All the results have been drawn on the frame, so it's time to display it.
+        cv2.imshow('Object detector', frame)
+
+        t2 = cv2.getTickCount()
+        time1 = (t2-t1)/freq
+        frame_rate_calc = 1/time1
+
+        # Press 'q' to quit
+        if cv2.waitKey(1) == ord('q'):
+            break
+
+    camera.release()
+
+cv2.destroyAllWindows()
+

core/add_product.py

@@ -2,7 +2,7 @@ from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker
 from models import Product, Base
 
-DATABASE_URL = 'sqlite:///local.db'
+DATABASE_URL = 'sqlite://../local.sqlite'
 
 engine = create_engine(DATABASE_URL)
 Session = sessionmaker(bind=engine)

core/add_user.py

@@ -2,7 +2,7 @@ from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker
 from models import Customer, Base, ENGLISH_LANGUAGE
 
-DATABASE_URL = 'sqlite:///local.db'
+DATABASE_URL = 'sqlite://../local.sqlite'
 
 engine = create_engine(DATABASE_URL)
 Session = sessionmaker(bind=engine)