diff --git a/Hough/HoughLabo.pdf b/Hough/HoughLabo.pdf
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diff --git a/Hough/hough.py b/Hough/hough.py
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--- /dev/null
+++ b/Hough/hough.py
@@ -0,0 +1,166 @@
+import sys
+from typing import List
+from typing import Tuple
+import numpy as np
+import numpy.typing as npt
+import math
+from PIL import Image
+from enum import Enum
+from matplotlib import pyplot as plt
+Img = npt.NDArray[np.uint8]
+HoughAcc = npt.NDArray[np.uint8]
+Line = Tuple[int,int]
+import cv2 as cv
+
+class Isotropy(Enum):
+ ISO_90 = 1
+ ISO_45 = 2
+
+
+def load_img(path:str) -> Img:
+ with Image.open(path) as im:
+ return np.array(im)
+
+def show_img(img: Img,axe:plt.axes) -> None:
+ if len(img.shape) == 2:
+ axe.imshow(img,cmap="gray",vmin=0,vmax=256)
+ else :
+ axe.imshow(img)
+
+def normalize(img: Img, new_range: Tuple[int, int] = (0, 255)) -> Img:
+ fMax = np.max(img)
+ fMin = np.min(img)
+ for y in range(img.shape[0]):
+ for x in range(img.shape[1]):
+ img[y][x] = round((img[y][x] - fMin) * ((new_range[1] - new_range[0])/(fMax-fMin)) + new_range[0])
+ return img.astype(int8)
+
+
+def rgb_to_gray(img: Img) -> Img:
+ res = np.zeros((img.shape[0],img.shape[1]))
+ iteNp = np.nditer(img,flags=["multi_index"])
+ for i in np.ndindex(img.shape[:2]):
+ res[i] = 0.2989*img[i][0] + 0.5870*img[i][1] + 0.1140*img[i][2]
+ return normalize(res)
+
+
+def xcorr(img: Img, kernel: Img) -> Img:
+ kLen = int(kernel.shape[0]/2)
+ xMax = img.shape[0]
+ yMax = img.shape[1]
+ xcorrRes = np.zeros((img.shape[0],img.shape[1]))
+ iteNp = np.nditer(img,flags=["multi_index"])
+ while not iteNp.finished :
+ x,y = iteNp.multi_index
+ #+1 since the right part is always taking the value before the one set ( 1,3) return an array between 1,2 and not 1,3
+ regToApply = img[x - kLen : x + kLen + 1, y - kLen : y + kLen + 1]
+ if not regToApply.shape == kernel.shape:
+ #We use those to start at the top left of the array
+ xCorner = x-kLen
+ yCorner = y-kLen
+ regToApply = np.zeros((kernel.shape[0],kernel.shape[1]))
+ for xK in range(kernel.shape[0]):
+ for yK in range(kernel.shape[1]):
+ if xCorner + xK >= xMax or xCorner + xK < 0 or yCorner + yK < 0 or yCorner + yK >= yMax :
+ regToApply[xK,yK] = 123
+ else:
+ regToApply[xK,yK] = img[xCorner + xK, yCorner + yK]
+ xcorrRes[x][y] = np.sum(np.multiply(regToApply,kernel))
+ iteNp.iternext()
+ return normalize(xcorrRes)
+
+
+def blur_avg(img: Img, blur_size: int) -> Img:
+ kernBlur = np.zeros((blur_size,blur_size),dtype=np.float64)
+ moy = math.pow(blur_size,2)
+ kernBlur[0:blur_size,0:blur_size] = 1/moy
+ return xcorr(img, kernBlur)
+
+def normalize(img: Img, new_range: Tuple[int, int] = (0, 255)) -> Img:
+ fMax = np.max(img)
+ fMin = np.min(img)
+ for y in range(img.shape[0]):
+ for x in range(img.shape[1]):
+ img[y][x] = 0 if img[y][x] == 0 else round((img[y][x] - fMin) * ((new_range[1] - new_range[0])/(fMax-fMin)) + new_range[0])
+ return img.astype(np.uint8)
+
+
+def laplace(img: Img, isotropy: Isotropy) -> Img:
+ kernLap = np.zeros((3,3))
+
+ if isotropy == Isotropy.ISO_45:
+ kernLap[1, 0:3] = -1
+ kernLap[0:3, 1] = -1
+ kernLap[1,1] = 4
+ else:
+ kernLap[0:3, 0:3] = -1
+ kernLap[1,1] = 8
+ return xcorr(img,kernLap)
+
+
+def hough_space(img: Img, angle_step: int = 1) -> HoughAcc:
+ step = range(0,180,angle_step)
+ xShape = img.shape[0]
+ yShape = img.shape[1]
+ accXSize = 180
+ accYSize = int(math.sqrt(pow(xShape,2) + pow(yShape,2)))
+ arrD = np.zeros((accXSize,accYSize),np.uint8)
+ for x in range(xShape):
+ for y in range(yShape):
+ if(img[x][y] == 255):
+ for ang in step:
+ res = int(x * math.cos(math.radians(ang)) + y * math.sin(math.radians(ang)))
+ #resAng = int(ang/angle_step)
+ arrD[ang,res] += 1
+ return arrD
+
+def hough_lines(acc: HoughAcc) -> List[Line]:
+ resLine = list(tuple())
+ for x in range(acc.shape[0]):
+ for y in range(acc.shape[1]):
+ if(acc[x,y] == 255):
+ resLine.append((x,y))
+ return resLine
+
+def pretraitement(img: Img) -> Img:
+ kernOC = np.ones((3,3),np.uint8 )
+
+ if(len(img.shape) == 3):
+ img = rgb_to_gray(img)
+
+
+
+ img = blur_avg(img, 3)
+
+ #v = cv.Canny(originalImg,0,255)
+ img = laplace(img, Isotropy.ISO_90)
+
+
+ ret, img = cv.threshold(img,125,255, cv.THRESH_BINARY)
+
+ return img - cv.erode(img,kernOC)
+
+if(len(sys.argv) <= 1 ):
+ print("Not enough parameters")
+ exit()
+
+
+angWished = 1
+fog, axe = plt.subplots(2,2)
+
+originalImg = load_img(sys.argv[1])
+
+show_img(originalImg, axe[0,0])
+axe[0,0].set_title("Original")
+preTrait = pretraitement(originalImg)
+show_img(preTrait, axe[0,1])
+axe[0,1].set_title("Pre-Traitement")
+accumulator = hough_space(preTrait,angWished)
+ret, accSeuil = cv.threshold(accumulator,80,255,cv.THRESH_BINARY)
+show_img(accumulator, axe[1,0])
+lines = hough_lines(accSeuil)
+axe[1,0].set_title("Accumulateur \n (Pré-seuillage)")
+plt.show()
+
+#a = (- costheta) / sintheta
+#b = x*cosTheta + y*sinTheta
\ No newline at end of file
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diff --git a/OCR/ImageTest/9_7.png b/OCR/ImageTest/9_7.png
new file mode 100644
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diff --git a/OCR/ImageTest/9_8.png b/OCR/ImageTest/9_8.png
new file mode 100644
index 0000000000000000000000000000000000000000..2729eb0fb0f937a1d355f3f78660815b384b58ba
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diff --git a/OCR/ImageTest/9_9.png b/OCR/ImageTest/9_9.png
new file mode 100644
index 0000000000000000000000000000000000000000..e49be9716ea7805de952f3925748b11998cf91aa
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diff --git a/OCR/InputOCR.html b/OCR/InputOCR.html
new file mode 100644
index 0000000000000000000000000000000000000000..4eed73804e1c496dd2e0244370b4dc849acede61
--- /dev/null
+++ b/OCR/InputOCR.html
@@ -0,0 +1,75 @@
+<!DOCTYPE html>
+<html>
+
+<body>
+
+ <canvas id="myCanvas" width="20" height="20" style="width:200px; height:200px; border:1px solid #c3c3c3;">
+ Your browser does not support the canvas element.
+ </canvas>
+ <input type="submit" id="download" value="Téléchargé">
+ <input type="submit" id="clear" value="Clear">
+
+ <script>
+ var canvas = document.getElementById("myCanvas");
+ var ctx = canvas.getContext("2d");
+
+
+ document.getElementById('download').addEventListener('click', function (e) {
+ // Convert our canvas to a data URL
+ let canvasUrl = canvas.toDataURL();
+ // Create an anchor, and set the href value to our data URL
+ const createEl = document.createElement('a');
+ createEl.href = canvasUrl;
+
+ // This is the name of our downloaded file
+ createEl.download = "download-this-canvas";
+
+ // Click the download button, causing a download, and then remove it
+ createEl.click();
+ createEl.remove();
+ ctx.clearRect(0, 0, canvas.width, canvas.height);
+ });
+
+
+ document.getElementById('clear').addEventListener('click', function (e) {
+ ctx.clearRect(0, 0, canvas.width, canvas.height);
+ });
+
+ //Drawing Code
+
+ // last known position
+ var pos = { x: 0, y: 0 };
+
+ document.addEventListener('mousemove', draw);
+ document.addEventListener('mousedown', setPosition);
+ document.addEventListener('mouseenter', setPosition);
+
+ // new position from mouse event
+ function setPosition(e) {
+ pos.x = e.clientX / 10;
+ pos.y = e.clientY / 10;
+ console.log(pos.x + " \ " + pos.y + "|||" + e.clientX + " \ " + e.clientY)
+ }
+
+
+ function draw(e) {
+ // mouse left button must be pressed
+ if (e.buttons !== 1) return;
+
+ ctx.beginPath(); // begin
+
+ ctx.lineWidth = 1;
+ ctx.lineCap = 'round';
+ ctx.strokeStyle = '#000000';
+
+ ctx.moveTo(pos.x, pos.y); // from
+ setPosition(e);
+ ctx.lineTo(pos.x, pos.y); // to
+
+ ctx.stroke(); // draw it!
+ }
+ </script>
+
+</body>
+
+</html>
\ No newline at end of file
diff --git a/Serie5/Ex1.py b/Serie5/Ex1.py
index 62387360a7a81292e9f3d195f053c5d30a8ffaa2..2e61405c21f3ae9bcb1b4803c296bcc379a7d0d9 100644
--- a/Serie5/Ex1.py
+++ b/Serie5/Ex1.py
@@ -24,6 +24,9 @@ raw = load_img("hepia.png")
kernCV = np.ones((3,3),np.uint8 )
kernOC = np.ones((5,5),np.uint8 )
+print(type(raw))
+print(type(raw[0,0]))
+print(type(kernOC))
fog, axe = plt.subplots(5,2)
axe[0,0].imshow(raw,cmap="gray",vmin=0,vmax=256)
@@ -51,4 +54,6 @@ axe[4,0].imshow(cExt,cmap="gray",vmin=0,vmax=256)
#Contour Intérieur
cInt = closeOpenCV - cv.erode(closeOpenCV,kernCV)
axe[4,1].imshow(cInt,cmap="gray",vmin=0,vmax=256)
-plt.show()
\ No newline at end of file
+plt.show()
+
+