code-for-object-detection

pip install opencv-python numpy pandas

#Object DetectionOn this pageSetupImports and function definitionsExample useHelper functions for downloading images and for visualization.Apply moduleMore images #Run in Google ColabView on GitHubDownload notebookSee TF Hub models #This Colab demonstrates use of a TF-Hub module trained to perform object detection. #SetupImports and function definitions

For running inference on the TF-Hub module.

import tensorflow as tf

import tensorflow_hub as hub

For downloading the image.

import matplotlib.pyplot as plt import tempfile from six.moves.urllib.request import urlopen from six import BytesIO

For drawing onto the image.

import numpy as np from PIL import Image from PIL import ImageColor from PIL import ImageDraw from PIL import ImageFont from PIL import ImageOps

For measuring the inference time.

import time

Print Tensorflow version

print(tf.version)

Check available GPU devices.

print("The following GPU devices are available: %s" % tf.test.gpu_device_name())

def display_image(image): fig = plt.figure(figsize=(20, 15)) plt.grid(False) plt.imshow(image)

def download_and_resize_image(url, new_width=256, new_height=256, display=False): _, filename = tempfile.mkstemp(suffix=".jpg") response = urlopen(url) image_data = response.read() image_data = BytesIO(image_data) pil_image = Image.open(image_data) pil_image = ImageOps.fit(pil_image, (new_width, new_height), Image.ANTIALIAS) pil_image_rgb = pil_image.convert("RGB") pil_image_rgb.save(filename, format="JPEG", quality=90) print("Image downloaded to %s." % filename) if display: display_image(pil_image) return filename

def draw_bounding_box_on_image(image, ymin, xmin, ymax, xmax, color, font, thickness=4, display_str_list=()): """Adds a bounding box to an image.""" draw = ImageDraw.Draw(image) im_width, im_height = image.size (left, right, top, bottom) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height) draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=thickness, fill=color)

If the total height of the display strings added to the top of the bounding

box exceeds the top of the image, stack the strings below the bounding box

instead of above.

display_str_heights = [font.getsize(ds)[1] for ds in display_str_list]

Each display_str has a top and bottom margin of 0.05x.

total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)

if top > total_display_str_height: text_bottom = top else: text_bottom = top + total_display_str_height

Reverse list and print from bottom to top.

for display_str in display_str_list[::-1]: text_width, text_height = font.getsize(display_str) margin = np.ceil(0.05 * text_height) draw.rectangle([(left, text_bottom - text_height - 2 * margin), (left + text_width, text_bottom)], fill=color) draw.text((left + margin, text_bottom - text_height - margin), display_str, fill="black", font=font) text_bottom -= text_height - 2 * margin

def draw_boxes(image, boxes, class_names, scores, max_boxes=10, min_score=0.1): """Overlay labeled boxes on an image with formatted scores and label names.""" colors = list(ImageColor.colormap.values())

try: font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf", 25) except IOError: print("Font not found, using default font.") font = ImageFont.load_default()

for i in range(min(boxes.shape[0], max_boxes)): if scores[i] >= min_score: ymin, xmin, ymax, xmax = tuple(boxes[i]) display_str = "{}: {}%".format(class_names[i].decode("ascii"), int(100 * scores[i])) color = colors[hash(class_names[i]) % len(colors)] image_pil = Image.fromarray(np.uint8(image)).convert("RGB") draw_bounding_box_on_image( image_pil, ymin, xmin, ymax, xmax, color, font, display_str_list=[display_str]) np.copyto(image, np.array(image_pil)) return image

image_url="http://thefinestmagazine.com/wp-content/uploads/2016/02/Rubix_The-Finest_Langham-chicago-41.jpg"

downloaded_image_path = download_and_resize_image(image_url, 1280, 856, True)

module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"

detector = hub.load(module_handle).signatures['default']

def load_img(path): img = tf.io.read_file(path) img = tf.image.decode_jpeg(img, channels=3) return img

def run_detector(detector, path): img = load_img(path)

converted_img = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...] start_time = time.time() result = detector(converted_img) end_time = time.time()

result = {key:value.numpy() for key,value in result.items()}

print("Found %d objects." % len(result["detection_scores"])) print("Inference time: ", end_time-start_time)

image_with_boxes = draw_boxes( img.numpy(), result["detection_boxes"], result["detection_class_entities"], result["detection_scores"])

display_image(image_with_boxes)

run_detector(detector, downloaded_image_path)

image_urls = [

"https://lp-cms-production.imgix.net/2020-11/AlamyRF_T3PACR.jpg",

"https://p1.pxfuel.com/preview/83/409/187/road-city-city-street-traffic.jpg",

]

def detect_img(image_url): start_time = time.time() image_path = download_and_resize_image(image_url, 640, 480) run_detector(detector, image_path) end_time = time.time() print("Inference time:",end_time-start_time)

detect_img(image_urls[0]) detect_img(image_urls[1])

image_urls = [

"https://bsmedia.business-standard.com/media-handler.php?mediaPath=https://bsmedia.business-standard.com/_media/bs/img/article/2021-02/05/full/1612508290-954.jpg&width=1200"]

def detect_img(image_url): start_time = time.time() image_path = download_and_resize_image(image_url, 640, 480) run_detector(detector, image_path) end_time = time.time() print("Inference time:",end_time-start_time) detect_img(image_urls[0])