Create app.py

app.py

@@ -0,0 +1,85 @@
+
+import numpy as np
+import gradio as gr
+from tensorflow.keras.models import load_model
+import imutils
+import matplotlib.pyplot as plt
+import cv2
+import numpy as np
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+
+PROB_THRESHOLD = 0.4 # Minimum probably to show results.
+class Model:
+    def __init__(self, model_filepath):
+        self.graph_def = tensorflow.compat.v1.GraphDef()
+        self.graph_def.ParseFromString(model_filepath.read_bytes())
+
+        input_names, self.output_names = self._get_graph_inout(self.graph_def)
+        assert len(input_names) == 1 and len(self.output_names) == 3
+        self.input_name = input_names[0]
+        self.input_shape = self._get_input_shape(self.graph_def, self.input_name)
+
+    def predict(self, image_filepath):
+        image = Image.fromarray(image_filepath).resize(self.input_shape)
+        input_array = np.array(image, dtype=np.float32)[np.newaxis, :, :, :]
+
+        with tensorflow.compat.v1.Session() as sess:
+            tensorflow.import_graph_def(self.graph_def, name='')
+            out_tensors = [sess.graph.get_tensor_by_name(o + ':0') for o in self.output_names]
+            outputs = sess.run(out_tensors, {self.input_name + ':0': input_array})
+            return {name: outputs[i][np.newaxis, ...] for i, name in enumerate(self.output_names)}
+
+    @staticmethod
+    def _get_graph_inout(graph_def):
+        input_names = []
+        inputs_set = set()
+        outputs_set = set()
+
+        for node in graph_def.node:
+            if node.op == 'Placeholder':
+                input_names.append(node.name)
+
+            for i in node.input:
+                inputs_set.add(i.split(':')[0])
+            outputs_set.add(node.name)
+
+        output_names = list(outputs_set - inputs_set)
+        return input_names, output_names
+
+    @staticmethod
+    def _get_input_shape(graph_def, input_name):
+        for node in graph_def.node:
+            if node.name == input_name:
+                return [dim.size for dim in node.attr['shape'].shape.dim][1:3]
+
+def print_outputs(outputs, gambar):
+  image = gambar
+  assert set(outputs.keys()) == set(['detected_boxes', 'detected_classes', 'detected_scores'])
+  l, t, d = image.shape
+  labelopen = open("labels.txt", 'r')
+  labels = [line.split(',') for line in labelopen.readlines()]
+  for box, class_id, score in zip(outputs['detected_boxes'][0], outputs['detected_classes'][0], outputs['detected_scores'][0]):
+    if score > PROB_THRESHOLD:
+      print(f"Label: {class_id}, Probability: {score:.5f}, box: ({box[0]:.5f}, {box[1]:.5f}) ({box[2]:.5f}, {box[3]:.5f})")
+      x = box[0] * t
+      y = box[1] * l
+      h = box[2] * t
+      w =  box[3] * l
+      result_image = cv2.rectangle(image, (int(x), int(y)), (int(h), int(w)),  (255,215,0), 3)
+      cv2.putText(result_image, labels[int(class_id)][0], (int(x), int(y)-10), fontFace = cv2.FONT_HERSHEY_SIMPLEX, fontScale = 0.5, color = (255,215,0), thickness = 2)
+  return result_image
+
+
+
+
+def main(gambar):
+    p = pathlib.Path("model.pb")
+    #b = pathlib.Path(gambar)
+    model = Model(p)
+    outputs = model.predict(gambar)
+    
+    return print_outputs(outputs, gambar)
+
+demo = gr.Interface(main, gr.Image(shape=(500, 500)), "image")
+demo.launch()