88 lines
2.8 KiB
Python
88 lines
2.8 KiB
Python
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import tensorflow as tf
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import sys
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import time
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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 common
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import config
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import model
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import csv
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model=model.model(config.input_model)
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rouge=(0, 0, 255)
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vert=(0, 255, 0)
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if True:
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dir=config.dir_images
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fichier="training_set.csv"
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test=False
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else:
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dir=config.dir_images_test
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fichier="test_set.csv"
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test=True
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checkpoint=tf.train.Checkpoint(model=model)
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checkpoint.restore(tf.train.latest_checkpoint("./training/"))
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with open(fichier, newline='') as csvfile:
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lignes=csv.reader(csvfile, delimiter=',')
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for ligne in lignes:
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print("LIGNE:", ligne)
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print("XXX", dir+ligne[0])
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img_originale=cv2.imread(dir+ligne[0])
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if img_originale is None:
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continue
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print("WWW", ligne[0], dir+ligne[0], img_originale.shape)
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H, W, C=img_originale.shape
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mm_pixel=float(ligne[1])
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img=cv2.resize(img_originale, (config.largeur, config.hauteur))
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img2=img.copy()
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img=np.array(img, dtype=np.float32)/255
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img=np.expand_dims(img[:, :, 0], axis=-1)
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predictions=model(np.array([img]))
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x, y, grand_axe, petit_axe, angle=predictions[0]
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cv2.ellipse(img2, (x*config.norm, y*config.norm), (petit_axe*config.norm/2, grand_axe*config.norm/2), angle*180, 0., 360., rouge, 2)
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print("Prediction", np.array(predictions[0]))
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if test is False:
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f_ellipse=ligne[0].split('.')[0]+"_Annotation.png"
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image_ellipse=cv2.imread(dir+f_ellipse)
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if image_ellipse is None:
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print("Fichier absent", dir+f_ellipse)
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continue
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img_ellipse_f_=image_ellipse[:, :, 0]
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contours, hierarchy=cv2.findContours(img_ellipse_f_, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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(x_, y_), (ma_, MA_), a_=cv2.fitEllipse(contours[0])
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cv2.ellipse(img_originale, (int(x_), int(y_)), (int(ma_/2), int(MA_/2)), a_, 0., 360., vert, 3)
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cv2.ellipse(img_originale, (x*W, y*W), (petit_axe*W/2, grand_axe*W/2), angle*180, 0., 360., rouge, 2)
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x=float(x*W*mm_pixel)
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y=float(y*W*mm_pixel)
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axis_x=float(grand_axe*W*mm_pixel/2)
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axis_y=float(petit_axe*W*mm_pixel/2)
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r=180.
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r_2=r/2
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if angle>=0.5:
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angle=angle*r-r_2
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else:
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angle=angle*r+r_2
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HC=np.pi*np.sqrt(2*(axis_x**2+axis_y**2))
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cv2.putText(img_originale, "HC: {:5.2f}mm".format(HC), (20, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255), 2)
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print("{},{:f},{:f},{:f},{:f},{:f} HC: {:f}mm".format(ligne[0], x, y, axis_x, axis_y, angle, HC))
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if len(ligne)==3:
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cv2.putText(img_originale, "HC: {:5.2f}mm".format(float(ligne[2])), (20, 60), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 2)
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print("HC: {}mm prediction: {:f}mm".format(ligne[2], HC))
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cv2.imshow("Image originale", img_originale)
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cv2.imshow("Inference", img2)
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key=cv2.waitKey()&0xFF
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if key==ord('q'):
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quit()
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