class Model: """ Class that contains the model and all its functions """ def __init__(self, model_path): """ Initialization function @ model_path : path to the model """ # Declare detection graph self.detection_graph = tf.Graph() # Load the model into the tensorflow graph with self.detection_graph.as_default(): od_graph_def = tf.compat.v1.GraphDef() with tf.io.gfile.GFile(model_path, 'rb') as file: serialized_graph = file.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') # Create a session from the detection graph self.sess = tf.compat.v1.Session(graph=self.detection_graph) def predict(self,img): """ Get the predicition results on 1 frame @ img : our img vector """ # Expand dimensions since the model expects images to have shape: [1, None, None, 3] img_exp = np.expand_dims(img, axis=0) # Pass the inputs and outputs to the session to get the results (boxes, scores, classes) = self.sess.run([self.detection_graph.get_tensor_by_name('detection_boxes:0'), self.detection_graph.get_tensor_by_name('detection_scores:0'), self.detection_graph.get_tensor_by_name('detection_classes:0')],feed_dict={self.detection_graph.get_tensor_by_name('image_tensor:0'): img_exp}) return (boxes, scores, classes)

# The error in the provided code is not entirely clear since the syntax should generally be fine. # Potential issues that could cause such an error might involve extra indentation or other surrounding errors. # Here's a corrected and standalone example of its usage within a loop to ensure correctness. for i in range(len(classes)): # Ensure the condition checks properly if int(classes[i]) == 1 and scores[i] > 0.75: print(f"Person detected with score: {scores[i]}")

width, height, _ = image.shape

def compute_perspective_transform(corner_points,width,height,image): """ Compute the transformation matrix @ corner_points : 4 corner points selected from the image @ height, width : size of the image return : transformation matrix and the transformed image """ # Create an array out of the 4 corner points corner_points_array = np.float32(corner_points) # Create an array with the parameters (the dimensions) required to build the matrix img_params = np.float32([[0,0],[width,0],[0,height],[width,height]]) # Compute and return the transformation matrix matrix = cv2.getPerspectiveTransform(corner_points_array,img_params) img_transformed = cv2.warpPerspective(image,matrix,(width,height)) return matrix,img_transformed

def compute_point_perspective_transformation(matrix,list_downoids): """ Apply the perspective transformation to every ground point which have been detected on the main frame. @ matrix : the 3x3 matrix @ list_downoids : list that contains the points to transform return : list containing all the new points """ # Compute the new coordinates of our points list_points_to_detect = np.float32(list_downoids).reshape(-1, 1, 2) transformed_points = cv2.perspectiveTransform(list_points_to_detect, matrix) # Loop over the points and add them to the list that will be returned transformed_points_list = list() for i in range(0,transformed_points.shape[0]): transformed_points_list.append([transformed_points[i][0][0],transformed_points[i][0][1]]) return transformed_points_list

# Check if 2 or more people have been detected (otherwise no need to detect) if len(transformed_downoids) >= 2: # Iterate over every possible 2 by 2 between the points combinations list_indexes = list(itertools.combinations(range(len(transformed_downoids)), 2)) for i,pair in enumerate(itertools.combinations(transformed_downoids, r=2)): # Check if the distance between each combination of points is less than the minimum distance chosen if math.sqrt( (pair[0][0] - pair[1][0])**2 + (pair[0][1] - pair[1][1])**2 ) < int(distance_minimum): # Change the colors of the points that are too close from each other to red change_color_topview(pair) # Get the equivalent indexes of these points in the original frame and change the color to red index_pt1 = list_indexes[i][0] index_pt2 = list_indexes[i][1] change_color_originalframe(index_pt1,index_pt2)