Create new file

app.py

@@ -0,0 +1,38 @@
+import cv2
+import gradio as gr
+from PIL import Image
+
+with open('coco.names', 'r') as f:
+    classes = f.read().splitlines()
+
+net = cv2.dnn.readNetFromDarknet('yolov4.cfg', 'yolov4.weights')
+
+model = cv2.dnn_DetectionModel(net)
+model.setInputParams(scale=1 / 255, size=(416, 416), swapRB=True)
+
+def detect(img):
+    img = Image.fromarray(img)
+    classIds, scores, boxes = model.detect(img, confThreshold=0.6, nmsThreshold=0.4)
+
+    for (classId, score, box) in zip(classIds, scores, boxes):
+        cv2.rectangle(img, (box[0], box[1]), (box[0] + box[2], box[1] + box[3]),
+                      color=(0, 255, 0), thickness=2)
+        text = '%s: %.2f' % (classes[classId], score)
+        cv2.putText(img, text, (box[0], box[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 1,
+                    color=(0, 255, 0), thickness=2)
+
+    return img
+
+image_in = gr.components.Image()
+image_out = gr.components.Image()
+
+classes_to_show = gr.components.Textbox(placeholder="e.g. person, boat", label="Classes to use (empty means all classes)")
+
+Iface = gr.Interface(
+    fn=detect,
+    inputs=image_in,
+    outputs=image_out,
+
+    title="Object Detection with YOLOS",
+    description=description,
+).launch()