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

##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 # 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} # Give location of the image to be read. # "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']) # Saving a original image and shape orig = image.copy() (origH, origW) = image.shape[:2] # set the new height and width to default 320 by using args #dictionary. (newW, newH) = (args["width"], args["height"]) # Calculate the ratio between original and new image for both height and weight. # This ratio will be used to translate bounding box location on the original image. rW = origW / float(newW) rH = origH / float(newH) # resize the original image to new dimensions image = cv2.resize(image, (newW, newH)) (H, W) = image.shape[:2] # 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) # load the pre-trained EAST model for text detection net = cv2.dnn.readNet(args["east"]) # We would like to get two outputs from the EAST model. # 1. Probabilty scores for the region whether that contains text or not. # 2. Geometry of the text -- Coordinates of the bounding box detecting a text # The following two layer need to pulled from EAST model for achieving this. layerNames = [ "feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"] # Forward pass the blob from the image to get the desired output layers net.setInput(blob) (scores, geometry) = net.forward(layerNames) ## 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) # 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)) # Display the image with bounding box and recognized text orig_image = orig.copy() # 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()