add app.py

app.py

@@ -0,0 +1,89 @@
+import cv2
+import khandy
+import numpy as np
+import gradio as gr
+from PIL import Image
+from insectid import InsectDetector
+from insectid import InsectIdentifier
+
+
+def inference(filename):
+    detector = InsectDetector()
+    identifier = InsectIdentifier()
+    image = khandy.imread(filename)
+
+    if image is None:
+        return None
+
+    if max(image.shape[:2]) > 1280:
+        image = khandy.resize_image_long(image, 1280)
+
+    image_for_draw = image.copy()
+    image_height, image_width = image.shape[:2]
+    boxes, confs, classes = detector.detect(image)
+
+    for box, conf, class_ind in zip(boxes, confs, classes):
+        box = box.astype(np.int32)
+        box_width = box[2] - box[0] + 1
+        box_height = box[3] - box[1] + 1
+
+        if box_width < 30 or box_height < 30:
+            continue
+
+        cropped = khandy.crop_or_pad(image, box[0], box[1], box[2], box[3])
+        results = identifier.identify(cropped)
+        print(results[0])
+        prob = results[0]['probability']
+
+        if prob < 0.10:
+            text = 'Unknown'
+        else:
+            text = '{}: {:.2f}%'.format(
+                results[0]['latin_name'],
+                100.0 * results[0]['probability']
+            )
+
+        position = [box[0] + 2, box[1] - 20]
+        position[0] = min(max(position[0], 0), image_width)
+        position[1] = min(max(position[1], 0), image_height)
+        cv2.rectangle(
+            image_for_draw,
+            (box[0], box[1]),
+            (box[2], box[3]),
+            (0, 255, 0),
+            2
+        )
+
+        image_for_draw = khandy.draw_text(
+            image_for_draw,
+            text,
+            position,
+            font='simsun.ttc',
+            font_size=15
+        )
+
+    return Image.fromarray(image_for_draw[:, :, ::-1], mode='RGB')
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("Image"):
+        gr.Markdown("## Insect Inference on Image")
+        with gr.Row():
+            image_input = gr.Image(
+                type='filepath',
+                label="Input Image",
+                source="upload"
+            )
+
+            image_output = gr.Image(
+                type='pil',
+                label="Output Image",
+                source="canvas"
+            )
+
+        text_button = gr.Button("Detect")
+
+    text_button.click(inference, inputs=image_input, outputs=image_output)
+
+
+demo.launch()

requirements.txt

@@ -3,4 +3,5 @@ numpy>=1.11.1
 lxml
 requests
 onnxruntime
-Pillow
+Pillow
+gradio