AirSim_Get_Raw_Image_and_Object_Detection

AirSim_get_raw_img

import airsim #pip install airsim
import cv2
import numpy as np
# for car use CarClient()
client = airsim.CarClient()

def transform_input(responses):
    img1d = np.fromstring(responses[0].image_data_uint8, dtype=np.uint8)

    #img1d = 255 / np.maximum(np.ones(img1d.size), img1d)

    img_rgba = img1d.reshape(responses[0].height, responses[0].width, 3)

    from PIL import Image

    image = Image.fromarray(img_rgba)

    im_final = np.array(image.convert('RGB'))

    return im_final

while(True):
    png_image = client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.Scene, False, False)])
    png_image = transform_input(png_image)
    png_image = cv2.resize(png_image, dsize=None, fx=3, fy=3)
    cv2.imshow('img',png_image)

    key = cv2.waitKey(1)
    if (key == 27 or key == ord('q') or key == ord('x')):
        break
# do something with image

AirSim_object_detection_with_yolov3

import airsim #pip install airsim
import cv2
import numpy as np
# for car use CarClient()
client = airsim.CarClient()

def transform_input(responses):
    img1d = np.fromstring(responses[0].image_data_uint8, dtype=np.uint8)

    #img1d = 255 / np.maximum(np.ones(img1d.size), img1d)

    img_rgba = img1d.reshape(responses[0].height, responses[0].width, 3)

    from PIL import Image

    image = Image.fromarray(img_rgba)

    im_final = np.array(image.convert('RGB'))

    return im_final

# Load Yolo
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))

while(True):
    # Loading image
    img = client.simGetImages([airsim.ImageRequest("0", airsim.ImageType.Scene, False, False)])
    img = transform_input(img)
    img = cv2.resize(img, None, fx=3, fy=3)
    height, width, channels = img.shape

    # Detecting objects
    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(output_layers)

    # Showing informations on the screen
    class_ids = []
    confidences = []
    boxes = []
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                # Object detected
                center_x = int(detection[0] * width)
                center_y = int(detection[1] * height)
                w = int(detection[2] * width)
                h = int(detection[3] * height)
                # Rectangle coordinates
                x = int(center_x - w / 2)
                y = int(center_y - h / 2)
                boxes.append([x, y, w, h])
                confidences.append(float(confidence))
                class_ids.append(class_id)

    indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

    font = cv2.FONT_HERSHEY_PLAIN
    for i in range(len(boxes)):
        if i in indexes:
            x, y, w, h = boxes[i]
            label = str(classes[class_ids[i]])
            color = colors[i]
            cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
            cv2.putText(img, label, (x, y + 30), font, 1, color, 3)

    #png_image = cv2.resize(png_image, dsize=None, fx=3, fy=3)
    cv2.imshow('img',img)

    key = cv2.waitKey(1)
    if (key == 27 or key == ord('q') or key == ord('x')):
        break
# do something with image

yolov3_for_single_img

import numpy as np
import cv2

# Load Yolo
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))

# Loading image
img = cv2.imread("dog.jpg")
img = cv2.resize(img, None, fx=0.4, fy=0.4)
height, width, channels = img.shape

# Detecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            # Object detected
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            # Rectangle coordinates
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)
            boxes.append([x, y, w, h])
            confidences.append(float(confidence))
            class_ids.append(class_id)

indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)

font = cv2.FONT_HERSHEY_PLAIN
for i in range(len(boxes)):
    if i in indexes:
        x, y, w, h = boxes[i]
        label = str(classes[class_ids[i]])
        color = colors[i]
        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)
        cv2.putText(img, label, (x, y + 30), font, 1, color, 3)
img = cv2.resize(img, None, fx=2.5, fy=2.5)
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()