Projets:Machine a lire IA

Description du projet

Le but est de créer une petite machine à lire portable capable d’acquérir le texte à partir d'une capture d'image et de le lire au moyen d’une synthèse vocale.

Cahier des charges

Pour une première version le texte est dactylographié .

L’utilisateur doit pouvoir :

• Déclencher l’acquisition de la page
• Lancer la lecture et la mettre en pause
• Régler la langue de l’OCR Français ou Anglais (pour une première version)
• Régler le niveau sonore du texte lu
• Régler le débit de la synthèse vocale
• Copier le texte lu dans le presse-papier
• L’envoyer par mail automatiquement à une adresse prédéfinie
• Atteindre une fenêtre de configuration si nécessaire

La solution doit être compacte basée sur une petite caméra reliée à un Raspberry PI 3 ou 4. Muni d’une batterie.

Les message audio seront émis :

• Soit par un petit HP intégré au boitier
• Soit via une oreillette sans fil

En option :

• il sera aussi possible de prévoir un support pour fixer la solution sur un support de bureau.
• La solution pourra permettre de lire une plaque de rue en condition de plein jour ou un nom de salle de réunion en intérieur

Équipe (Porteur de projet et contributeurs)

• Porteurs du projet : François LB
• Concepteurs/contributeurs : Mickaël Le Cabellec
• Animateur (coordinateur du projet) :
• Fabmanager référent :
• Responsable de documentation

Fichiers source

//

1. 1. Loading the necessary packages

import cv2 import numpy as np import pytesseract from imutils.object_detection import non_max_suppression from matplotlib import pyplot as plt

1. Creating argument dictionary for the default arguments needed in the code.

args = {"image": "../input/text-detection/example-images/Example-images/ex24.jpg",

       "east": "../input/text-detection/east_text_detection.pb", "min_confidence": 0.5, "width": 320, "height": 320}

1. Give location of the image to be read.
2. "Example-images/ex24.jpg" image is being loaded here.

cv2.namedWindow("preview") vc = cv2.VideoCapture(0)

if vc.isOpened(): # try to get the first frame

   rval, frame = vc.read()

else:

   rval = False

while rval:

   cv2.imshow("preview", frame)
   rval, frame = vc.read()
   key = cv2.waitKey(20)
   if key == 27: # exit on ESC
       break

cv2.destroyWindow("preview")

args['image'] = "../input/text-detection/example-images/Example-images/ex24.jpg" image = cv2.imread(args['image'])

1. Saving a original image and shape

orig = image.copy() (origH, origW) = image.shape[:2]

1. set the new height and width to default 320 by using args #dictionary.

(newW, newH) = (args["width"], args["height"])

1. Calculate the ratio between original and new image for both height and weight.
2. This ratio will be used to translate bounding box location on the original image.

rW = origW / float(newW) rH = origH / float(newH)

1. resize the original image to new dimensions

image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2]

1. construct a blob from the image to forward pass it to EAST model

blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),

                            (123.68, 116.78, 103.94), swapRB=True, crop=False)

1. load the pre-trained EAST model for text detection

net = cv2.dnn.readNet(args["east"])

1. We would like to get two outputs from the EAST model.
2. 1. Probabilty scores for the region whether that contains text or not.
3. 2. Geometry of the text -- Coordinates of the bounding box detecting a text
4. The following two layer need to pulled from EAST model for achieving this.

layerNames = [

   "feature_fusion/Conv_7/Sigmoid",
   "feature_fusion/concat_3"]

1. Forward pass the blob from the image to get the desired output layers

net.setInput(blob) (scores, geometry) = net.forward(layerNames)

1. 1. Returns a bounding box and probability score if it is more than minimum confidence

def predictions(prob_score, geo):

   (numR, numC) = prob_score.shape[2:4]
   boxes = []
   confidence_val = []

   # loop over rows
   for y in range(0, numR):
       scoresData = prob_score[0, 0, y]
       x0 = geo[0, 0, y]
       x1 = geo[0, 1, y]
       x2 = geo[0, 2, y]
       x3 = geo[0, 3, y]
       anglesData = geo[0, 4, y]

       # loop over the number of columns
       for i in range(0, numC):
           if scoresData[i] < args["min_confidence"]:
               continue

           (offX, offY) = (i * 4.0, y * 4.0)

           # extracting the rotation angle for the prediction and computing the sine and cosine
           angle = anglesData[i]
           cos = np.cos(angle)
           sin = np.sin(angle)

           # using the geo volume to get the dimensions of the bounding box
           h = x0[i] + x2[i]
           w = x1[i] + x3[i]

           # compute start and end for the text pred bbox
           endX = int(offX + (cos * x1[i]) + (sin * x2[i]))
           endY = int(offY - (sin * x1[i]) + (cos * x2[i]))
           startX = int(endX - w)
           startY = int(endY - h)

           boxes.append((startX, startY, endX, endY))
           confidence_val.append(scoresData[i])

   # return bounding boxes and associated confidence_val
   return (boxes, confidence_val)

1. Find predictions and apply non-maxima suppression

(boxes, confidence_val) = predictions(scores, geometry) boxes = non_max_suppression(np.array(boxes), probs=confidence_val)

for (startX, startY, endX, endY) in boxes:

   # scale the coordinates based on the respective ratios in order to reflect bounding box on the original image
   startX = int(startX * rW)
   startY = int(startY * rH)
   endX = int(endX * rW)
   endY = int(endY * rH)

   # extract the region of interest
   r = orig[startY:endY, startX:endX]

   # configuration setting to convert image to string.
   configuration = ("-l eng --oem 1 --psm 8")
   ##This will recognize the text from the image of bounding box
   text = pytesseract.image_to_string(r, config=configuration)

   # append bbox coordinate and associated text to the list of results
   results.append(((startX, startY, endX, endY), text))

1. Display the image with bounding box and recognized text

orig_image = orig.copy()

1. Moving over the results and display on the image

for ((start_X, start_Y, end_X, end_Y), text) in results:

   # display the text detected by Tesseract
   print("{}\n".format(text))

   # Displaying text
   text = "".join([x if ord(x) < 128 else "" for x in text]).strip()
   cv2.rectangle(orig_image, (start_X, start_Y), (end_X, end_Y),
                 (0, 0, 255), 2)
   cv2.putText(orig_image, text, (start_X, start_Y - 30),
               cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

plt.imshow(orig_image) plt.title('Output') plt.show()