Isaacgonzales commited on
Commit
5a7c59c
1 Parent(s): d78a11f

add time result

Browse files
Files changed (2) hide show
  1. app.py +3 -3
  2. model.py +6 -4
app.py CHANGED
@@ -23,9 +23,9 @@ from model import prediction
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  def predict(img):
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- prediction_img, text = prediction(img)
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- return str(text), prediction_img,
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  sample_images = ["dataset/" + name for name in os.listdir("dataset")]
@@ -33,6 +33,6 @@ sample_images = ["dataset/" + name for name in os.listdir("dataset")]
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  gr.Interface(fn=predict,
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  inputs=[gr.Image(label="image à tester" ,type="filepath")],
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- outputs=[gr.Textbox(label="analyse"), gr.Image(label ="résultat") ],
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  css="footer {visibility: hidden} body}, .gradio-container {background-color: white}",
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  examples=sample_images).launch(server_name="0.0.0.0", share=False)
 
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  def predict(img):
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+ prediction_img, text, t_process = prediction(img)
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+ return t_process, str(text), prediction_img,
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  sample_images = ["dataset/" + name for name in os.listdir("dataset")]
 
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  gr.Interface(fn=predict,
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  inputs=[gr.Image(label="image à tester" ,type="filepath")],
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+ outputs=[gr.Textbox(label="temps"), gr.Textbox(label="analyse"), gr.Image(label ="résultat") ],
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  css="footer {visibility: hidden} body}, .gradio-container {background-color: white}",
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  examples=sample_images).launch(server_name="0.0.0.0", share=False)
model.py CHANGED
@@ -8,6 +8,7 @@ from torchvision import transforms
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  from PIL import Image
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  import torch
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  import cv2
 
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  model_recog = load_from_checkpoint("weights/parseq/last.ckpt").eval().to("cpu")
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  img_transform = SceneTextDataModule.get_transform(model_recog.hparams.img_size)
@@ -21,6 +22,7 @@ SHAPE_Y = 384
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  def prediction(image_path):
 
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  image = cv2.imread(image_path)
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  image_original = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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  preprocessing_fn = smp.encoders.get_preprocessing_fn('resnet50')
@@ -42,11 +44,11 @@ def prediction(image_path):
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  p = model_recog(image).softmax(-1)
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  pred, p = model_recog.tokenizer.decode(p)
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- print(f'{image_path}: {pred[0]}')
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-
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-
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- return img_vis, pred[0]
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  from PIL import Image
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  import torch
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  import cv2
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+ from time import process_time
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  model_recog = load_from_checkpoint("weights/parseq/last.ckpt").eval().to("cpu")
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  img_transform = SceneTextDataModule.get_transform(model_recog.hparams.img_size)
 
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  def prediction(image_path):
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+ t_start = process_time()
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  image = cv2.imread(image_path)
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  image_original = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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  preprocessing_fn = smp.encoders.get_preprocessing_fn('resnet50')
 
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  p = model_recog(image).softmax(-1)
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  pred, p = model_recog.tokenizer.decode(p)
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+ t_stop = process_time()
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+ t_process = t_stop - t_start
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+ print(f'{image_path}: {pred[0]}, {t_process} seconds')
 
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+ return img_vis, pred[0], t_process
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