yolo_main.py

# -*- coding: utf-8 -*-
"""
Created on Sun Nov  4 03:53:18 2018

@author: imlab
"""
import cv2

import numpy as np

        
def tinu(image):
    start_flag = True
    def get_output_layers(net):
    
        layer_names = net.getLayerNames()
        
        output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
    
        return output_layers
    
    # function to draw bounding box on the detected object with class name
    def draw_bounding_box(img, class_id, confidence, x, y, x_plus_w, y_plus_h):
    
        label = str(classes[class_id])
    
        color = COLORS[class_id]
    
        cv2.rectangle(img, (x,y), (x_plus_w,y_plus_h), color, 2)
    
        cv2.putText(img, label, (x-10,y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
        
        
    Width = image.shape[1]
    Height = image.shape[0]
    scale = 0.00392

    if start_flag:
        start_flag = False
        classes = 'models/coco.names'
        weights = 'models/yolov3-tiny.weights'
        config = 'models/yolov3-tiny.cfg'
    
    with open(classes, 'r') as f:
        classes = [line.strip() for line in f.readlines()]
        
    COLORS = np.random.uniform(0, 255, size=(len(classes), 3))
    
    # read pre-trained model and config file
    
    net = cv2.dnn.readNet(weights, config)
    
    # create input blob 
    blob = cv2.dnn.blobFromImage(image, scale, (416,416), (0,0,0), True, crop=False)
    
    # set input blob for the network
    net.setInput(blob)
    
    outs = net.forward(get_output_layers(net))

    # initialization
    class_ids = []
    confidences = []
    boxes = []
    conf_threshold = 0.5
    nms_threshold = 0.4
    

    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]

            if confidence > 0.5:
                center_x = int(detection[0] * Width)
                center_y = int(detection[1] * Height)
                w = int(detection[2] * Width)
                h = int(detection[3] * Height)
                x = center_x - w / 2
                y = center_y - h / 2
                class_ids.append(class_id)
                confidences.append(float(confidence))
                boxes.append([x, y, w, h])

    
    # apply non-max suppression
    
    indices = cv2.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
    
#    # go through the detections remaining
#    # after nms and draw bounding box
#    
    for i in indices:
        i = i[0]
        box = boxes[i]
        x = box[0]
        y = box[1]
        w = box[2]
        h = box[3]
        

        draw_bounding_box(image, class_ids[i], confidences[i], round(x), round(y), round(x+w), round(y+h))
        
    return image , confidences , class_ids