105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
import tensorflow as tf
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from tensorflow.keras import layers, models
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import csv
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import random
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import cv2
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import numpy as np
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import math
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import csv
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import random
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import config
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def rotateImage(image, angle):
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image_center=tuple(np.array(image.shape[1::-1])/2)
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rot_mat=cv2.getRotationMatrix2D(image_center, angle, 1.0)
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result=cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)
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return result
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def complete_dataset(image, image_ellipse, tab_images, tab_labels):
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contours, hierarchy=cv2.findContours(image_ellipse[:, :, 0], cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours) is not 2:
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return 1
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else:
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if len(contours[0])<6 or len(contours[1])<6:
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return 1
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(x1, y1), (ma1, MA1), a1=cv2.fitEllipse(contours[0])
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(x2, y2), (ma2, MA2), a2=cv2.fitEllipse(contours[1])
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x=(x1+x2)/2
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y=(y1+y2)/2
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ma=(ma1+ma2)/2
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MA=(MA1+MA2)/2
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a=(a1+a2)/2
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tab_images.append(image[:, :, 0])
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tab_labels.append([x/config.norm, y/config.norm, MA/config.norm, ma/config.norm, a/180])
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return 0
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def prepare_data(fichier):
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with open(fichier, newline='') as csvfile:
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lignes=csv.reader(csvfile, delimiter=',')
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next(lignes)
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tab_images=[]
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tab_labels=[]
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nbr=0
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for ligne in lignes:
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image_orig=cv2.imread(config.dir_images+ligne[0])
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if image_orig is None:
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print("Fichier absent", config.dir_images+ligne[0])
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continue
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f_ellipse=ligne[0].split('.')[0]+"_Annotation.png"
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image_ellipse_orig=cv2.imread(config.dir_images+f_ellipse)
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if image_ellipse_orig is None:
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print("Fichier absent", config.dir_images+f_ellipse)
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continue
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for angle in range(0, 360, 30):
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if np.random.randint(2)==0:
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h, w, c=image_orig.shape
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H=int(h*1.4)
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W=int(w*1.4)
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h_shift=np.random.randint(H-h)
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w_shift=np.random.randint(W-w)
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i=np.zeros(shape=(H, W, c), dtype=np.uint8)
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i[h_shift:h_shift+h, w_shift:w_shift+w, :]=image_orig
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image_orig2=i
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i=np.zeros(shape=(H, W, c), dtype=np.uint8)
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i[h_shift:h_shift+h, w_shift:w_shift+w, :]=image_ellipse_orig
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image_ellipse_orig2=i
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else:
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image_orig2=image_orig
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image_ellipse_orig2=image_ellipse_orig
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image=cv2.resize(image_orig2, (config.largeur, config.hauteur), interpolation=cv2.INTER_AREA)
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image_ellipse=cv2.resize(image_ellipse_orig2, (config.largeur, config.hauteur), interpolation=cv2.INTER_AREA)
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img_r=rotateImage(image, angle)
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#if np.random.randint(3)==0:
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# kernel_blur=np.random.randint(2)*2+1
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# img_r=cv2.GaussianBlur(img_r, (kernel_blur, kernel_blur), 0)
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bruit=np.random.randn(config.hauteur, config.largeur, 3)*random.randint(1, 50)
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img_r=np.clip(img_r+bruit, 0, 255).astype(np.uint8)
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img_ellipse=rotateImage(image_ellipse, angle)
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nbr+=complete_dataset(img_r, img_ellipse, tab_images, tab_labels)
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img_f=cv2.flip(img_r, 0)
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img_ellipse_f=cv2.flip(img_ellipse, 0)
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nbr+=complete_dataset(img_f, img_ellipse_f, tab_images, tab_labels)
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img_f=cv2.flip(img_r, 1)
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img_ellipse_f=cv2.flip(img_ellipse, 1)
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nbr+=complete_dataset(img_f, img_ellipse_f, tab_images, tab_labels)
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img_f=cv2.flip(img_r, -1)
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img_ellipse_f=cv2.flip(img_ellipse, -1)
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nbr+=complete_dataset(img_f, img_ellipse_f, tab_images, tab_labels)
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print("Image(s) rejetée(s):", nbr)
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print("Nombre d'images:", len(tab_images))
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return tab_images, tab_labels
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