Initial commit

Tensorflow/tutoriel27/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
+
+images, labels, labels2=common.read_json('test.json', 5)
+images=np.array(images, dtype=np.float32)/255
+labels=np.array(labels, dtype=np.float32)
+index=np.random.permutation(len(images))
+images=images[index]
+labels=labels[index]
+
+model=model.model(config.nbr_classes, config.nbr_boxes, config.cellule_y, config.cellule_x)
+
+checkpoint=tf.train.Checkpoint(model=model)
+checkpoint.restore(tf.train.latest_checkpoint("./training/"))
+
+grid=np.meshgrid(np.arange(config.cellule_x, dtype=np.float32), np.arange(config.cellule_y, dtype=np.float32))
+grid=np.expand_dims(np.stack(grid, axis=-1), axis=2)
+grid=np.tile(grid, (1, 1, config.nbr_boxes, 1))
+
+for i in range(len(images)):
+  img=common.prepare_image(images[i], labels[i], False)
+  predictions=model(np.array([images[i]]))
+  
+  pred_boxes=predictions[0, :, :, :, 0:4]
+  pred_conf=common.sigmoid(predictions[0, :, :, :, 4])
+  pred_classes=common.softmax(predictions[0, :, :, :, 5:])
+  ids=np.argmax(pred_classes, axis=-1)
+
+  x_center=((grid[:, :, :, 0]+common.sigmoid(pred_boxes[:, :, :, 0]))*config.r_x)
+  y_center=((grid[:, :, :, 1]+common.sigmoid(pred_boxes[:, :, :, 1]))*config.r_y)
+  w=(np.exp(pred_boxes[:, :, :, 2])*config.anchors[:, 0]*config.r_x)
+  h=(np.exp(pred_boxes[:, :, :, 3])*config.anchors[:, 1]*config.r_y)
+
+  x_min=(x_center-w/2).astype(np.int32)
+  y_min=(y_center-h/2).astype(np.int32)
+  x_max=(x_center+w/2).astype(np.int32)
+  y_max=(y_center+h/2).astype(np.int32)
+  
+  tab_boxes=[]
+  conf=[]
+  for y in range(config.cellule_y):
+    for x in range(config.cellule_x):
+      for b in range(config.nbr_boxes):
+        if pred_conf[y, x, b]>0.10:
+          color=list(config.dict.values())[ids[y, x, b]]
+          cv2.circle(images[i], (x_center[y, x, b], y_center[y, x, b]), 1, color, 2)
+          cv2.rectangle(images[i], (x_min[y, x, b], y_min[y, x, b]), (x_max[y, x, b], y_max[y, x, b]), color, 1)
+          cv2.rectangle(images[i], (x_min[y, x, b], y_min[y, x, b]), (x_max[y, x, b], y_min[y, x, b]-15), color, cv2.FILLED)
+          cv2.putText(images[i], "{:3.0%}".format(pred_conf[y, x, b]), (x_min[y, x, b], y_min[y, x, b]-5), cv2.FONT_HERSHEY_COMPLEX_SMALL , 0.5, (255, 255, 255), 1)
+
+  cv2.imshow("Inference", images[i])
+  cv2.imshow("Bonne reponse", img)
+    
+  key=cv2.waitKey()&0xFF
+  if key==ord('q'):
+    quit()