Add application file and labels

app.py

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+import cv2
+import numpy as np
+import onnxruntime as rt
+import gradio as gr
+
+def image_preprocess(image):
+
+  img_height, img_width = image.shape[0:2]
+  image_converted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+
+  ih, iw = [input_size, input_size] # [input_size, input_size] = [640, 640]
+  h, w, _ = image.shape # [1944, 2592]
+
+  scale = min(iw/w, ih/h) # min(0.2469, 0.3292) = 0.2469
+  nw, nh = int(scale * w), int(scale * h) # [640, 480]
+  image_resized = cv2.resize(image_converted, (nw, nh))
+
+  image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0)
+  dw, dh = (iw - nw) // 2, (ih-nh) // 2 # [0, 80]
+  image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized # image_padded[80:256, 32:224]
+  image_padded = image_padded / 255.
+  # image_resized = image_resized / 255.
+  image_padded = image_padded[np.newaxis, ...].astype(np.float32)
+  image_padded = np.moveaxis(image_padded, -1, 1)
+
+
+  return image_padded, img_width, img_height, image
+  
+  
+def inference(image_data):
+  detections = sess.run(output_names, {input_name: image_data})
+  return detections
+  
+def draw_detections(img, box, score, class_id):
+
+    # Extract the coordinates of the bounding box
+    x1, y1, x2, y2 = box
+
+    # Retrieve the color for the class ID
+    color = color_palette_pred[class_id]
+
+    # Draw the bounding box on the image
+    cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, 3)
+
+    # Create the label text with class name and score
+    label = f'{classes[class_id]}: {score:.2f}'
+
+    # Calculate the dimensions of the label text
+    (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
+
+    # Calculate the position of the label text
+    label_x = x1
+    label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
+
+    # Draw a filled rectangle as the background for the label text
+    label_x = round(label_x)
+    label_y = round(label_y)
+    cv2.rectangle(img, (label_x, label_y - label_height), (label_x + label_width, label_y + label_height), color, cv2.FILLED)
+
+    # Draw the label text on the image
+    cv2.putText(img, label, (label_x, label_y), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 0), 1, cv2.LINE_AA)
+    
+    
+def postprocess(detections, img_width, img_height, input_size, original_image):
+  boxes = detections[1][0]
+  scores = detections[2][0]
+  class_ids = detections[3][0]
+  # width_scale = img_width / input_size
+  # height_scale = img_height / input_size
+
+  ih, iw = [input_size, input_size] # [input_size, input_size] = [640, 640]
+  h, w = img_height, img_width # [1944, 2592]
+  scale = min(iw/w, ih/h) # min(0.2469, 0.3292) = 0.2469
+  nw, nh = int(scale * w), int(scale * h) # [640, 480]
+  dw, dh = (iw - nw) // 2, (ih-nh) // 2 # [0, 80]
+  # dh, dw = 0, 0
+  # scale = 0.3292
+  new_boxes = []
+  for box, score, class_id in zip(boxes, scores, class_ids):
+
+    x1, y1, x2, y2 = box
+    x1 = (x1 - dw) / scale
+    y1 = (y1 - dh) / scale
+    x2 = (x2 - dw) / scale
+    y2 = (y2 - dh) / scale
+    box = [x1, y1, x2, y2]
+    draw_detections(original_image, box, score, class_id)
+    new_boxes.append(box)
+  return [class_ids, scores, new_boxes]
+  
+  
+# fix all the variables
+with open("coco_names.txt", "r") as f:
+    content = f.readlines()
+
+content = "".join(content)
+classes = content.split("\n")
+color_palette_pred = np.random.uniform(0, 255, size=(len(classes), 3))  
+  
+sess = rt.InferenceSession("yolov8n_coco_640.nms.onnx")
+
+outputs = sess.get_outputs()
+output_names = list(map(lambda output: output.name, outputs))
+input_name = sess.get_inputs()[0].name
+input_size = sess.get_inputs()[0].shape[2]
+
+
+def run(img_path):
+    image_data, img_width, img_height, original_image = image_preprocess(img_path)
+    detections = inference(image_data)
+    l = postprocess(detections, img_width, img_height, input_size, original_image)
+    return original_image  
+  
+demo = gr.Interface(
+    fn=run,
+    inputs=["image"],
+    outputs=["image"],
+)
+
+demo.launch()
+  

coco_names.txt

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+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+backpack
+umbrella
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+wine glass
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+dining table
+toilet
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush