app.py

import os
import tensorflow as tf
import tensorflow_hub as hub
# Load compressed models from tensorflow_hub
os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'

import matplotlib.pyplot as plt
import matplotlib as mpl

# For drawing onto the image.
import numpy as np
from tensorflow.python.ops.numpy_ops import np_config
np_config.enable_numpy_behavior()
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFont
import time

import streamlit as st

# For measuring the inference time.
import time

def run_detector(detector, path):
  # img = load_img_2(path)
  img = path
  
  converted_img  = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...]
 
  start_time = time.time()
  result = detector(converted_img)
  end_time = time.time()

  result = {key:value.numpy() for key,value in result.items()}

  # print("Found %d objects." % len(result["detection_scores"]))
  # print("Inference time: ", end_time-start_time)

  primer = format(result["detection_class_entities"][0]) + ' ' + format(round(result["detection_scores"][0]*100)) + '%'

  image_with_boxes = draw_boxes(
    img, result["detection_boxes"],
    result["detection_class_entities"], result["detection_scores"])

  display_image(image_with_boxes)
  return image_with_boxes, primer
  
def display_image(image):
  fig = plt.figure(figsize=(20, 15))
  plt.grid(False)
  plt.imshow(image)

def draw_bounding_box_on_image(image,
                               ymin,
                               xmin,
                               ymax,
                               xmax,
                               color,
                               font,
                               thickness=4,
                               display_str_list=()):
  """Adds a bounding box to an image."""
  draw = ImageDraw.Draw(image)
  im_width, im_height = image.size
  (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                ymin * im_height, ymax * im_height)
  draw.line([(left, top), (left, bottom), (right, bottom), (right, top),
             (left, top)],
            width=thickness,
            fill=color)

  # If the total height of the display strings added to the top of the bounding
  # box exceeds the top of the image, stack the strings below the bounding box
  # instead of above.
  display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]
  # Each display_str has a top and bottom margin of 0.05x.
  total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)

  if top > total_display_str_height:
    text_bottom = top
  else:
    text_bottom = top + total_display_str_height
  # Reverse list and print from bottom to top.
  for display_str in display_str_list[::-1]:
    text_width, text_height = font.getsize(display_str)
    margin = np.ceil(0.05 * text_height)
    draw.rectangle([(left, text_bottom - text_height - 2 * margin),
                    (left + text_width, text_bottom)],
                   fill=color)
    draw.text((left + margin, text_bottom - text_height - margin),
              display_str,
              fill="black",
              font=font)
    text_bottom -= text_height - 2 * margin

def draw_boxes(image, boxes, class_names, scores, max_boxes=10, min_score=0.4):
  """Overlay labeled boxes on an image with formatted scores and label names."""
  colors = list(ImageColor.colormap.values())

  try:
    font = ImageFont.truetype("./Roboto-Light.ttf", 24)
      
  except IOError:
    print("Font not found, using default font.")
    font = ImageFont.load_default()

  for i in range(min(boxes.shape[0], max_boxes)):
    if scores[i] >= min_score:
      ymin, xmin, ymax, xmax = tuple(boxes[i])
      display_str = "{}: {}%".format(class_names[i].decode("ascii"),
                                     int(100 * scores[i]))
      color = colors[hash(class_names[i]) % len(colors)]
      image_pil = Image.fromarray(np.uint8(image)).convert("RGB")
      draw_bounding_box_on_image(
          image_pil,
          ymin,
          xmin,
          ymax,
          xmax,
          color,
          font,
          display_str_list=[display_str])
      np.copyto(image, np.array(image_pil))
  return image

def main():
    image = Image.open('./itaca_logo_2.png')
    # image_hospital = Image.open('./ust.png')
    st.image(image,use_column_width=False)
    # st.sidebar.info('This app is created to detect objects in a picture')
    # st.sidebar.image(image_hospital)
    # st.sidebar.success('https://www.ust.com')
    st.title("Object Detector :sunglasses:")

    # filename = file_selector(FILE_PATH)

    img_file_buffer = st.file_uploader("Carga una imagen", type=["png", "jpg", "jpeg"])
    if img_file_buffer is not None:
        image = np.array(Image.open(img_file_buffer))    
        # st.image(image, caption="Imagen", use_column_width=True)

    module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1" 
    # module_handle = "https://tfhub.dev/google/openimages_v4/ssd/mobilenet_v2/1"
    
    detector = hub.load(module_handle).signatures['default']

    if st.button("Prediction"):
        # img, primero = run_detector(detector, filename)
        img, primero = run_detector(detector, image)
        # primero = run_detector(detector, image)
        st.success('The first image detected is: ' + primero)
        st.image(img, caption="Imagen", use_column_width=True)

        

if __name__ == '__main__':
    main()