94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
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import random
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import tensorflow as tf
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import csv
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import numpy as np
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import cv2
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import model
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fichier='ISIC2018_Task3_Training_GroundTruth/ISIC2018_Task3_Training_GroundTruth.csv'
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dir_images='ISIC2018_Task3_Training_Input/'
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labels=['Melanoma',
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'Melanocytic nevus',
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'Basal cell carcinoma',
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'Actinic keratosis',
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'Benign keratosis',
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'Dermatofibroma',
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'Vascular lesion']
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tab_images=[]
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tab_labels=[]
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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 bruit(image):
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h, w, c=image.shape
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n=np.random.randn(h, w, c)*random.randint(5, 30)
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return np.clip(image+n, 0, 255).astype(np.uint8)
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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, None)
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for ligne in lignes:
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label=np.array(ligne[1:], dtype=np.float32)
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img=cv2.imread(dir_images+ligne[0]+'.jpg')
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img=cv2.resize(img, (100, 75))
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if img is None:
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print("XXX")
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quit()
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tab_labels.append(label)
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tab_images.append(img)
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if label[1]:
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continue
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flag=0
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for angle in range(0, 360, 30):
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img_r=rotateImage(img, angle)
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if label[2] or label[3] or label[5] or label[6]:
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tab_labels.append(label)
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i=cv2.flip(img_r, 0)
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tab_images.append(i)
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if not flag%3 and (label[0] or label[4]):
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tab_labels.append(label)
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i=cv2.flip(img_r, 0)
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tab_images.append(i)
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flag+=1
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if label[2] or label[3] or label[5] or label[6]:
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tab_labels.append(label)
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i=cv2.flip(img_r, 1)
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tab_images.append(i)
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if label[5] or label[6]:
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tab_labels.append(label)
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i=cv2.flip(img_r, -1)
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tab_images.append(i)
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tab_labels=np.array(tab_labels, dtype=np.float32)
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tab_images=np.array(tab_images, dtype=np.float32)/255
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indices=np.random.permutation(len(tab_labels))
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tab_labels=tab_labels[indices]
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tab_images=tab_images[indices]
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print("SOMME", np.sum(tab_labels, axis=0))
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model=tf.keras.models.load_model('my_model/')
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for i in range(len(tab_images)):
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cv2.imshow("image", tab_images[i])
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prediction=model.predict(np.array([tab_images[i]], dtype=np.float32))
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print("Bonne reponse:{}, Reponse du réseau:{}".format(labels[np.argmax(tab_labels[i])], labels[np.argmax(prediction[0])]))
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key=cv2.waitKey()&0xFF
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if key==ord('q'):
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break
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cv2.destroyAllWindows()
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