Object Detection

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This Colab demonstrates use of a TF-Hub module trained to perform object detection.

Setup

Imports and function definitions

 2.16.1 The following GPU devices are available: 2024-03-09 13:48:48.238338: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

Example use

Helper functions for downloading images and for visualization.

Visualization code adapted from TF object detection API for the simplest required functionality.

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.LANCZOS) 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.getbbox(ds)[3] 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]: bbox = font.getbbox(display_str) text_width, text_height = bbox[2], bbox[3] 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

Apply module

Load a public image from Open Images v4, save locally, and display.

# By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg image_url = "https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg" downloaded_image_path = download_and_resize_image(image_url, 1280, 856, True) 
 Image downloaded to /tmpfs/tmp/tmpxk3tpk5k.jpg.

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Pick an object detection module and apply on the downloaded image. Modules:

  • FasterRCNN+InceptionResNet V2: high accuracy,
  • ssd+mobilenet V2: small and fast.
module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1" detector = hub.load(module_handle).signatures['default'] 
 INFO:tensorflow:Saver not created because there are no variables in the graph to restore INFO:tensorflow:Saver not created because there are no variables in the graph to restore 
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) 
 WARNING: All log messages before absl::InitializeLog() is called are written to STDERR W0000 00:00:1709992227.984468 76125 op_level_cost_estimator.cc:699] Error in PredictCost() for the op: op: "CropAndResize" attr { key: "T" value { type: DT_FLOAT } } attr { key: "extrapolation_value" value { f: 0 } } attr { key: "method" value { s: "bilinear" } } inputs { dtype: DT_FLOAT shape { dim { size: -2484 } dim { size: -2485 } dim { size: -2486 } dim { size: 1088 } } } inputs { dtype: DT_FLOAT shape { dim { size: -105 } dim { size: 4 } } } inputs { dtype: DT_INT32 shape { dim { size: -105 } } } inputs { dtype: DT_INT32 shape { dim { size: 2 } } value { dtype: DT_INT32 tensor_shape { dim { size: 2 } } int_val: 17 } } device { type: "CPU" vendor: "GenuineIntel" model: "111" frequency: 2299 num_cores: 32 environment { key: "cpu_instruction_set" value: "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2" } environment { key: "eigen" value: "3.4.90" } l1_cache_size: 32768 l2_cache_size: 262144 l3_cache_size: 47185920 memory_size: 268435456 } outputs { dtype: DT_FLOAT shape { dim { size: -105 } dim { size: 17 } dim { size: 17 } dim { size: 1088 } } } Found 100 objects. Inference time: 39.93564057350159

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More images

Perform inference on some additional images with time tracking.

image_urls = [ # Source: https://commons.wikimedia.org/wiki/File:The_Coleoptera_of_the_British_islands_(Plate_125)_(8592917784).jpg "https://upload.wikimedia.org/wikipedia/commons/1/1b/The_Coleoptera_of_the_British_islands_%28Plate_125%29_%288592917784%29.jpg", # By Américo Toledano, Source: https://commons.wikimedia.org/wiki/File:Biblioteca_Maim%C3%B3nides,_Campus_Universitario_de_Rabanales_007.jpg "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg/1024px-Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg", # Source: https://commons.wikimedia.org/wiki/File:The_smaller_British_birds_(8053836633).jpg "https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_%288053836633%29.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]) 
 Image downloaded to /tmpfs/tmp/tmp1ym56ptn.jpg. Found 100 objects. Inference time: 2.6752500534057617 Inference time: 2.8970775604248047

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detect_img(image_urls[1]) 
 Image downloaded to /tmpfs/tmp/tmpkkemjnhv.jpg. Found 100 objects. Inference time: 2.8261632919311523 Inference time: 3.0437986850738525

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detect_img(image_urls[2]) 
 Image downloaded to /tmpfs/tmp/tmpkpi72oyk.jpg. Found 100 objects. Inference time: 2.6092212200164795 Inference time: 2.888493776321411

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