180 lines
5.4 KiB
Python
180 lines
5.4 KiB
Python
from ultralytics import YOLO
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from flask import request, Flask, jsonify
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from waitress import serve
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from PIL import Image
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import onnxruntime as ort
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import numpy as np
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#my changes
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import os
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# Change the working directory to the script's directory
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os.chdir(script_dir)
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yolo_classes = ["b_fully_ripened",
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"b_half_ripened",
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"b_green",
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"l_fully_ripened",
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"l_half_ripened",
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"l_green"
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]
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#app start
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app = Flask(__name__)
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@app.route("/")
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def root():
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"""
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Site main page handler function.
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:return: Content of index.html file
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"""
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with open("index.html") as file:
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return file.read()
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@app.route("/detect", methods=["POST"])
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def detect():
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"""
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Handler of /detect POST endpoint
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Receives uploaded file with a name "image_file", passes it
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through YOLOv8 object detection network and returns and array
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of bounding boxes.
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:return: a JSON array of objects bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
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"""
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buf = request.files["image_file"]
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boxes, orientation = detect_objects_on_image(buf.stream)
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#print(boxes)
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#print(orientation)
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return jsonify(boxes)
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def detect_objects_on_image(buf):
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input, img_width, img_height = prepare_input(buf)
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output = run_model(input)
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orientation = get_orientation(buf)
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processed_output = process_output(output, img_width, img_height, orientation)
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return processed_output, orientation
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def prepare_input(buf):
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img = Image.open(buf)
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img_width, img_height = img.size
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img = img.resize((640, 640))
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img = img.convert("RGB")
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input = np.array(img)
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input = input.transpose(2, 0, 1)
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input = input.reshape(1, 3, 640, 640) / 255.0
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return input.astype(np.float32), img_width, img_height
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def run_model(input):
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model = ort.InferenceSession("best.onnx", providers=['CPUExecutionProvider'])
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outputs = model.run(["output0"], {"images":input})
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return outputs[0]
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def process_output(output, img_width, img_height, orientation):
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output = output[0].astype(float)
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output = output.transpose()
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boxes = []
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for row in output:
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prob = row[4:].max()
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if prob < 0.5:
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continue
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class_id = row[4:].argmax()
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label = yolo_classes[class_id]
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xc, yc, w, h = row[:4]
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x1 = (xc - w/2) / 640 * img_width
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y1 = (yc - h/2) / 640 * img_height
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x2 = (xc + w/2) / 640 * img_width
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y2 = (yc + h/2) / 640 * img_height
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boxes.append([x1, y1, x2, y2, label, prob])
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# Adjust boxes based on orientation
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adjusted_boxes = adjust_boxes_for_orientation(boxes, orientation, img_width, img_height)
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# Sort and apply non-max suppression as before
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adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
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result = []
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while len(adjusted_boxes) > 0:
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result.append(adjusted_boxes[0])
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adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_boxes[0]) < 0.7]
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return result
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def iou(box1,box2):
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return intersection(box1,box2)/union(box1,box2)
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def union(box1,box2):
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box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
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box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
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box1_area = (box1_x2-box1_x1)*(box1_y2-box1_y1)
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box2_area = (box2_x2-box2_x1)*(box2_y2-box2_y1)
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return box1_area + box2_area - intersection(box1,box2)
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def intersection(box1,box2):
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box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
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box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
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x1 = max(box1_x1,box2_x1)
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y1 = max(box1_y1,box2_y1)
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x2 = min(box1_x2,box2_x2)
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y2 = min(box1_y2,box2_y2)
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return (x2-x1)*(y2-y1)
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def get_orientation(image_path):
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with Image.open(image_path) as img:
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if hasattr(img, '_getexif'):
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exif_data = img._getexif()
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if exif_data is not None:
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return exif_data.get(274, 1) # Default to normal orientation
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return 1 # Default orientation if no EXIF data
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def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
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adjusted_boxes = []
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for box in boxes:
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x1, y1, x2, y2, label, prob = box
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# Apply transformations based on orientation
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if orientation == 3: # 180 degrees
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x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
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elif orientation == 6: # 270 degrees (or -90 degrees)
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x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
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elif orientation == 8: # 90 degrees
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x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
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adjusted_boxes.append([x1, y1, x2, y2, label, prob])
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return adjusted_boxes
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""" def detect_objects_on_image(buf):
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"""
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""""
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Function receives an image,
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passes it through YOLOv8 neural network
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and returns an array of detected objects
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and their bounding boxes
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:param buf: Input image file stream
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:return: Array of bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
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"""
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"""""
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model = YOLO("best.pt")
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results = model.predict(Image.open(buf))
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result = results[0]
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output = []
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for box in result.boxes:
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x1, y1, x2, y2 = [
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round(x) for x in box.xyxy[0].tolist()
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]
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class_id = box.cls[0].item()
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prob = round(box.conf[0].item(), 2)
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output.append([
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x1, y1, x2, y2, result.names[class_id], prob
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])
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return output
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"""
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serve(app, host='0.0.0.0', port=8080)
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