Create app.py

app.py

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+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()