Create app.py

app.py

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+import cv2
+import numpy as np
+from onnx import numpy_helper
+import onnx
+import os
+from PIL import Image
+from matplotlib.pyplot import imshow
+import onnxruntime as rt
+from scipy import special
+import colorsys
+import random
+import gradio as gr
+
+def image_preprocess(image, target_size, gt_boxes=None):
+
+    ih, iw = target_size
+    h, w, _ = image.shape
+
+    scale = min(iw/w, ih/h)
+    nw, nh = int(scale * w), int(scale * h)
+    image_resized = cv2.resize(image, (nw, nh))
+
+    image_padded = np.full(shape=[ih, iw, 3], fill_value=128.0)
+    dw, dh = (iw - nw) // 2, (ih-nh) // 2
+    image_padded[dh:nh+dh, dw:nw+dw, :] = image_resized
+    image_padded = image_padded / 255.
+
+    if gt_boxes is None:
+        return image_padded
+
+    else:
+        gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw
+        gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh
+        return image_padded, gt_boxes
+        
+input_size = 416
+
+os.system("wget https://github.com/AK391/models/raw/main/vision/object_detection_segmentation/yolov4/model/yolov4.onnx")
+
+# Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
+# other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
+# based on the build flags) when instantiating InferenceSession.
+# For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
+# rt.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
+sess = rt.InferenceSession("yolov4.onnx")
+
+outputs = sess.get_outputs()
+
+
+
+def get_anchors(anchors_path, tiny=False):
+    '''loads the anchors from a file'''
+    with open(anchors_path) as f:
+        anchors = f.readline()
+    anchors = np.array(anchors.split(','), dtype=np.float32)
+    return anchors.reshape(3, 3, 2)
+
+def postprocess_bbbox(pred_bbox, ANCHORS, STRIDES, XYSCALE=[1,1,1]):
+    '''define anchor boxes'''
+    for i, pred in enumerate(pred_bbox):
+        conv_shape = pred.shape
+        output_size = conv_shape[1]
+        conv_raw_dxdy = pred[:, :, :, :, 0:2]
+        conv_raw_dwdh = pred[:, :, :, :, 2:4]
+        xy_grid = np.meshgrid(np.arange(output_size), np.arange(output_size))
+        xy_grid = np.expand_dims(np.stack(xy_grid, axis=-1), axis=2)
+
+        xy_grid = np.tile(np.expand_dims(xy_grid, axis=0), [1, 1, 1, 3, 1])
+        xy_grid = xy_grid.astype(np.float)
+
+        pred_xy = ((special.expit(conv_raw_dxdy) * XYSCALE[i]) - 0.5 * (XYSCALE[i] - 1) + xy_grid) * STRIDES[i]
+        pred_wh = (np.exp(conv_raw_dwdh) * ANCHORS[i])
+        pred[:, :, :, :, 0:4] = np.concatenate([pred_xy, pred_wh], axis=-1)
+
+    pred_bbox = [np.reshape(x, (-1, np.shape(x)[-1])) for x in pred_bbox]
+    pred_bbox = np.concatenate(pred_bbox, axis=0)
+    return pred_bbox
+
+
+def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold):
+    '''remove boundary boxs with a low detection probability'''
+    valid_scale=[0, np.inf]
+    pred_bbox = np.array(pred_bbox)
+
+    pred_xywh = pred_bbox[:, 0:4]
+    pred_conf = pred_bbox[:, 4]
+    pred_prob = pred_bbox[:, 5:]
+
+    # # (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax)
+    pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5,
+                                pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1)
+    # # (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org)
+    org_h, org_w = org_img_shape
+    resize_ratio = min(input_size / org_w, input_size / org_h)
+
+    dw = (input_size - resize_ratio * org_w) / 2
+    dh = (input_size - resize_ratio * org_h) / 2
+
+    pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio
+    pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio
+
+    # # (3) clip some boxes that are out of range
+    pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]),
+                                np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1)
+    invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3]))
+    pred_coor[invalid_mask] = 0
+
+    # # (4) discard some invalid boxes
+    bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1))
+    scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1]))
+
+    # # (5) discard some boxes with low scores
+    classes = np.argmax(pred_prob, axis=-1)
+    scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]
+    score_mask = scores > score_threshold
+    mask = np.logical_and(scale_mask, score_mask)
+    coors, scores, classes = pred_coor[mask], scores[mask], classes[mask]
+
+    return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1)
+
+def bboxes_iou(boxes1, boxes2):
+    '''calculate the Intersection Over Union value'''
+    boxes1 = np.array(boxes1)
+    boxes2 = np.array(boxes2)
+
+    boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
+    boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
+
+    left_up       = np.maximum(boxes1[..., :2], boxes2[..., :2])
+    right_down    = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
+
+    inter_section = np.maximum(right_down - left_up, 0.0)
+    inter_area    = inter_section[..., 0] * inter_section[..., 1]
+    union_area    = boxes1_area + boxes2_area - inter_area
+    ious          = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps)
+
+    return ious
+
+def nms(bboxes, iou_threshold, sigma=0.3, method='nms'):
+    """
+    :param bboxes: (xmin, ymin, xmax, ymax, score, class)
+    Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf
+          https://github.com/bharatsingh430/soft-nms
+    """
+    classes_in_img = list(set(bboxes[:, 5]))
+    best_bboxes = []
+
+    for cls in classes_in_img:
+        cls_mask = (bboxes[:, 5] == cls)
+        cls_bboxes = bboxes[cls_mask]
+
+        while len(cls_bboxes) > 0:
+            max_ind = np.argmax(cls_bboxes[:, 4])
+            best_bbox = cls_bboxes[max_ind]
+            best_bboxes.append(best_bbox)
+            cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]])
+            iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4])
+            weight = np.ones((len(iou),), dtype=np.float32)
+
+            assert method in ['nms', 'soft-nms']
+
+            if method == 'nms':
+                iou_mask = iou > iou_threshold
+                weight[iou_mask] = 0.0
+
+            if method == 'soft-nms':
+                weight = np.exp(-(1.0 * iou ** 2 / sigma))
+
+            cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight
+            score_mask = cls_bboxes[:, 4] > 0.
+            cls_bboxes = cls_bboxes[score_mask]
+
+    return best_bboxes
+
+def read_class_names(class_file_name):
+    '''loads class name from a file'''
+    names = {}
+    with open(class_file_name, 'r') as data:
+        for ID, name in enumerate(data):
+            names[ID] = name.strip('\n')
+    return names
+
+def draw_bbox(image, bboxes, classes=read_class_names("coco.names"), show_label=True):
+    """
+    bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates.
+    """
+
+    num_classes = len(classes)
+    image_h, image_w, _ = image.shape
+    hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
+    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
+    colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
+
+    random.seed(0)
+    random.shuffle(colors)
+    random.seed(None)
+
+    for i, bbox in enumerate(bboxes):
+        coor = np.array(bbox[:4], dtype=np.int32)
+        fontScale = 0.5
+        score = bbox[4]
+        class_ind = int(bbox[5])
+        bbox_color = colors[class_ind]
+        bbox_thick = int(0.6 * (image_h + image_w) / 600)
+        c1, c2 = (coor[0], coor[1]), (coor[2], coor[3])
+        cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
+
+        if show_label:
+            bbox_mess = '%s: %.2f' % (classes[class_ind], score)
+            t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0]
+            cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1)
+            cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX,
+                        fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA)
+
+    return image
+ 
+def inference(img):   
+  original_image = cv2.imread(img)
+  original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
+  original_image_size = original_image.shape[:2]
+  
+  image_data = image_preprocess(np.copy(original_image), [input_size, input_size])
+  image_data = image_data[np.newaxis, ...].astype(np.float32)
+  
+  print("Preprocessed image shape:",image_data.shape) # shape of the preprocessed input
+  
+  output_names = list(map(lambda output: output.name, outputs))
+  input_name = sess.get_inputs()[0].name
+  
+  detections = sess.run(output_names, {input_name: image_data})
+  print("Output shape:", list(map(lambda detection: detection.shape, detections)))
+  
+  ANCHORS = "./yolov4_anchors.txt"
+  STRIDES = [8, 16, 32]
+  XYSCALE = [1.2, 1.1, 1.05]
+  
+  ANCHORS = get_anchors(ANCHORS)
+  STRIDES = np.array(STRIDES)
+  
+  
+  
+  pred_bbox = postprocess_bbbox(detections, ANCHORS, STRIDES, XYSCALE)
+  bboxes = postprocess_boxes(pred_bbox, original_image_size, input_size, 0.25)
+  bboxes = nms(bboxes, 0.213, method='nms')
+  image = draw_bbox(original_image, bboxes)
+  
+  image = Image.fromarray(image)
+  return image
+ 
+title="YOLOv4"
+description="YOLOv4 optimizes the speed and accuracy of object detection. It is two times faster than EfficientDet. It improves YOLOv3's AP and FPS by 10% and 12%, respectively, with mAP50 of 52.32 on the COCO 2017 dataset and FPS of 41.7 on Tesla 100." 
+examples=[["example.png"]]
+gr.Interface(inference,gr.inputs.Image(type="filepath"),gr.outputs.Image(type="pil"),title=title,description=description,examples=examples).launch()
+