Initial commit

Tensorflow/concours_foetus/inference.py

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