tensorflow-human-detection.py

# Code adapted from Tensorflow Object Detection Framework
# https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
# Tensorflow Object Detection Detector

import numpy as np
import tensorflow as tf
import cv2
import time


class DetectorAPI:
    def __init__(self, path_to_ckpt):
        self.path_to_ckpt = path_to_ckpt

        self.detection_graph = tf.Graph()
        with self.detection_graph.as_default():
            od_graph_def = tf.GraphDef()
            with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)
                tf.import_graph_def(od_graph_def, name='')

        self.default_graph = self.detection_graph.as_default()
        self.sess = tf.Session(graph=self.detection_graph)

        # Definite input and output Tensors for detection_graph
        self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        self.detection_boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
        self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')

    def processFrame(self, image):
        # Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image, axis=0)
        # Actual detection.
        start_time = time.time()
        (boxes, scores, classes, num) = self.sess.run(
            [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
            feed_dict={self.image_tensor: image_np_expanded})
        end_time = time.time()

        print("Elapsed Time:", end_time-start_time)

        im_height, im_width,_ = image.shape
        boxes_list = [None for i in range(boxes.shape[1])]
        for i in range(boxes.shape[1]):
            boxes_list[i] = (int(boxes[0,i,0] * im_height),
                        int(boxes[0,i,1]*im_width),
                        int(boxes[0,i,2] * im_height),
                        int(boxes[0,i,3]*im_width))

        return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])

    def close(self):
        self.sess.close()
        self.default_graph.close()

if __name__ == "__main__":
    model_path = '/path/to/faster_rcnn_inception_v2_coco_2017_11_08/frozen_inference_graph.pb'
    odapi = DetectorAPI(path_to_ckpt=model_path)
    threshold = 0.7
    cap = cv2.VideoCapture('/path/to/input/video')

    while True:
        r, img = cap.read()
        img = cv2.resize(img, (1280, 720))

        boxes, scores, classes, num = odapi.processFrame(img)

        # Visualization of the results of a detection.

        for i in range(len(boxes)):
            # Class 1 represents human
            if classes[i] == 1 and scores[i] > threshold:
                box = boxes[i]
                cv2.rectangle(img,(box[1],box[0]),(box[3],box[2]),(255,0,0),2)

        cv2.imshow("preview", img)
        key = cv2.waitKey(1)
        if key & 0xFF == ord('q'):
            break

Thank you for this
But can any one share with me this type of project that only download and run directly .please I need help @engineerabbas675@gmail.com

@BrandonGene can't really know why, but I would bet on your file specs, since you're using Windows.

The problem could be BOM, Byte-Order Mark. Depending on the IDE you use, UTF-8 files could be saved with BOM, and failing at a UTF8 decoder that can't handle it.

If it's BOM the problem, I'm not sure, then save your file as simple UTF8 (normallly, not With BOM). Might wanna have an advance IDE like Sublime to do it, or IDE that uses normal one by default.

If it's not BOM the problem, the you can still try another IDE that can handle your file normally. Also it could be incomplete file, check the file size online and offline, or the md5 or sha1 hash value and check that you have the same file that can't be decoded.

Thanks, I solved it!
Can I use this model to learn more complex human images?