Duplicate from coutant/yolov8-detection

Co-authored-by: Stephane Coutant <[email protected]>

.gitattributes

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.gitignore

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+.idea
+*.pt

README.md

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+---
+title: Yolov8 Detection
+emoji: 📚
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 3.16.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: coutant/yolov8-detection
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import cv2
+import gradio as gr
+from ultralytics import YOLO
+
+def inference(path:str, threshold:float=0.6):
+    print("trying inference with path", path)
+    if path is None:
+        return None,0
+    model = YOLO('yolov8m.pt') # Caution new API since Ultralytics 8.0.43
+    # [0]  # only considering class 'person' and not the 79 other classes...
+    outputs = model.predict(source=path, classes= [0], show=False, conf=threshold) # new API with Ultralytics 8.0.43. Accepts 'show', 'classes', 'stream', 'conf' (default is 0.25)
+    image = cv2.imread(path)
+    counter = 0
+    for output in outputs:  # mono item batch
+        conf = output.boxes.conf  # the tensor of detection confidences
+        xyxy = output.boxes.xyxy
+        cls = output.boxes.cls # 0 is 'person' and 5 is 'bus' 16 is dog
+        nb=cls.size(dim=0)
+        for i in range(nb):
+            box = xyxy[i]
+            if conf[i]<threshold:
+                break
+            cv2.rectangle(
+                image,
+                (int(box[0]), int(box[1])),
+                (int(box[2]), int(box[3])),
+                color=(0, 0, 255),
+                thickness=2,
+            )
+            counter+=1
+    return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), counter
+
+gr.Interface(
+    fn = inference,
+    inputs = [ gr.components.Image(type="filepath", label="Input"), gr.Slider(minimum=0.5, maximum=0.9, step=0.05, value=0.7, label="Confidence threshold") ],
+    outputs = [ gr.components.Image(type="numpy", label="Output"), gr.Label(label="nb of persons detected for given confidence threshold") ],
+    title="Person detection with YOLO v8",
+    description="Person detection, you can tweak the corresponding confidence threshold. Good results even when face not visible. New API since Ultralytics 8.0.43.",
+    examples=[ ['data/businessmen-612.jpg'], ['data/businessmen-back.jpg']],
+    allow_flagging="never"
+).launch(debug=True, enable_queue=True)

requirements.txt

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+torch
+torchvision
+ultralytics
+gradio
+opencv-python
+numpy