Added app.py

app.py

@@ -0,0 +1,214 @@
+# Imports
+import cv2
+from detector import Detector
+from pathlib import Path
+import gradio as gr
+import os
+import numpy as np
+import csv
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Choose weights, names and config file
+chosen_weights = "cfg/vessels_tyv4.weights"
+chosen_config_file = "cfg/vessels_tyv4.cfg"
+chosen_names = "cfg/vessel.names"
+
+with open(chosen_names, "r") as f:
+    classes = [line.strip() for line in f.readlines()]
+labels = np.array(classes, dtype=str)
+
+# Function for analyzing images
+def analyze_image(selected_image, chosen_conf_thresh, chosen_nms_thresh):
+
+    # Delete existing output files
+    if os.path.exists("Classes.csv"):
+        os.remove("Classes.csv")
+
+    if selected_image is None:
+        raise RuntimeError("No image found!")
+
+    print("Starting image scan")
+    
+    # Initialize the detector
+    detector = Detector(weights = str(chosen_weights), config_file = str(chosen_config_file), classes_file = chosen_names,  conf_thresh = chosen_conf_thresh, nms_thresh = chosen_nms_thresh)
+
+    # Detect the image
+    img_det, classes_id, scores, boxes = detector.detect(selected_image)
+
+    class_names = []
+
+    for _class in classes_id:
+        class_names.append(labels[_class])
+
+    tags = []
+
+    for i in range(len(class_names)):
+        tags.append([str(class_names[i]), str(boxes[i][0]), str(boxes[i][1]), str(boxes[i][2]), str(boxes[i][3]), str(scores[i])])
+        
+    print("Image scan finished succefully.")
+
+    # Save tags in a csv file
+    with open("Classes.csv", "w") as f:
+        write = csv.writer(f)  
+        write.writerow(["Class", "X", "Y", "Width", "Height", "Score"])      
+        write.writerows(tags)
+        f.close()
+
+    return img_det, "Classes.csv", tags[:10]
+
+# Function for analyzing video
+def analyze_video(selected_video, chosen_conf_thresh, chosen_nms_thresh, start_sec, duration):
+
+    # Delete existing output files
+    if os.path.exists("demo_film.mp4"):
+        os.remove("demo_film.mp4")
+    if os.path.exists("output.mp4"):
+        os.remove("output.mp4")
+    if os.path.exists("Classes.csv"):
+        os.remove("Classes.csv")
+
+    if selected_video is None:
+        raise RuntimeError("No video found!")
+
+    print("Starting video scan")
+
+    # Capture the video input
+    video = cv2.VideoCapture(selected_video)
+    ret, frame = video.read()
+    if not ret: # Checking
+        raise RuntimeError("Cannot read video stream!")
+        
+    # Calculate start and end frame 
+    total_frames = round(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = video.get(cv2.CAP_PROP_FPS) 
+    start_frame = round(start_sec * fps)
+    end_frame = round(start_frame + (duration * fps))
+
+    # Initialize the detector
+    detector = Detector(weights = str(chosen_weights), config_file = str(chosen_config_file), classes_file = chosen_names,  conf_thresh = chosen_conf_thresh, nms_thresh = chosen_nms_thresh)
+
+    frame_id = 0
+    vid_out = []
+    save_file_name = "demo_film.mp4"
+
+    tags = []
+    unique_objects = []
+    
+    if start_frame > total_frames:
+        raise RuntimeError("Start second is out of bounds!")
+
+    while True:
+        # Read the image
+        ret, frame = video.read()
+
+        if not ret: # Error or end of stream heck
+            break
+        if frame is None: continue
+        
+        if  start_frame <= frame_id <= end_frame:
+            class_names = []
+            # Detect the image
+            img_det, classes_id, scores, boxes = detector.detect(frame)
+
+            for _class in classes_id:
+                class_names.append(labels[_class])
+
+                if unique_objects.count(labels[_class]) == 0:
+                    unique_objects.append(labels[_class])
+
+            for i in range(len(class_names)):
+                tags.append([str(class_names[i]), str(boxes[i][0]), str(boxes[i][1]), str(boxes[i][2]), str(boxes[i][3]), str(scores[i]), str(frame_id)])
+
+            # video writer
+            if frame_id == start_frame:
+                Height, Width = img_det.shape[:2]
+                fps = video.get(cv2.CAP_PROP_FPS) if 15 < video.get(cv2.CAP_PROP_FPS) <= 30 else 15
+                vid_out = cv2.VideoWriter(save_file_name, cv2.VideoWriter_fourcc(*"MP4V"), fps, (Width, Height)) 
+            vid_out.write(img_det)
+
+        if frame_id > end_frame:
+            break
+
+        frame_id += 1
+
+    # Release videos
+    video.release()
+    vid_out.release()
+
+    with open("Classes.csv", "w") as f:      
+        write = csv.writer(f) 
+        write.writerow(["Class", "X", "Y", "Width", "Height", "Score","Frame"])     
+        write.writerows(tags)
+        f.close()
+
+    plt.switch_backend("agg")
+
+    if end_frame > total_frames:
+        end_frame = total_frames
+
+    fig = plt.figure()
+
+    df = pd.DataFrame(tags, columns = ["Class", "X", "Y", "Width", "Height", "Score", "Frame"])
+
+    for unique_object in unique_objects:
+        object_array = df[df["Class"].str.fullmatch(unique_object)==True]
+        obj_per_frame = []
+
+        for i in range(start_frame, end_frame + 1):
+            temp_array = []
+            temp_array = object_array[object_array["Frame"].astype("str").str.fullmatch(str(i))==True]
+            rows = temp_array.shape[0]
+            obj_per_frame.append(rows)
+
+        plt.plot(list(range(start_frame, end_frame+1)), obj_per_frame, label = unique_object)
+
+    plt.title("Objects per frame")
+    plt.ylabel("Objects")
+    plt.xlabel("Frame")
+    plt.legend()
+
+    print("Video scan finished succefully.")
+
+    # Changes video fourcc to h264 so that it can be displayed in the browser
+    os.system("ffmpeg -i demo_film.mp4 -vcodec libx264 -f mp4 output.mp4")
+
+    return "output.mp4", fig, "Classes.csv", tags[:10]
+
+
+# Dradio interfaces take mandatory parameters an input function, the input type(s) and output type(s)
+# Demo is hosted on http://localhost:7860/
+
+# Examples
+image_examples = [
+    ["examples/horses.png", 0.20, 0.40],
+    ["examples/basketball.png", 0.25, 0.40],
+]
+video_examples =[["examples/scene_from_series.mp4", 0.25, 0.40, 0, 10]]
+
+# Image interface
+image_interface = gr.Interface(fn = analyze_image,
+                                   inputs = [gr.Image(label = "Image"),
+                                   gr.Slider(0, 1, value = 0.25, label = "Confidence Threshold"),
+                                   gr.Slider(0, 1, value = 0.40, label = "Non Maxima Supression threshold")],
+                                   outputs = [gr.Image(label="Image"), gr.File(label="All classes"), gr.Dataframe(label="Ten first classes", headers=["Class", "X", "Y", "Width", "Height", "Score"])],
+                                   allow_flagging = False,
+                                   cache_examples = False,
+                                   examples = image_examples)
+
+# Video interface
+video_interface = gr.Interface(fn = analyze_video,
+                                  inputs = [gr.Video(label = "Video"),
+                                  gr.Slider(0, 1, value = 0.25, label = "Confidence Threshold"),
+                                  gr.Slider(0, 1, value = 0.40, label = "Non Maxima Supression threshold"),
+                                  gr.Slider(0, 60, value = 0, label = "Start Second", step = 1),
+                                  gr.Slider(1, 10, value = 4, label = "Duration", step = 1)],
+                                  outputs = [gr.Video(label="Video"), gr.Plot(label="Objects per frame"), gr.File(label="All classes"), gr.Dataframe(label=" Ten first classes", headers=["Class", "X", "Y", "Width", "Height", "Score", "Frame"])],
+                                  allow_flagging = False,
+                                  cache_examples = False,
+                                  examples = video_examples)
+
+gr.TabbedInterface(
+    [video_interface, image_interface],
+    ["Scan Videos", "Scan Images"]
+).launch()

cfg/vessels_tyv4.cfg

@@ -0,0 +1,281 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=8
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.00261
+burn_in=1000
+max_batches = 80200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers=-1
+groups=2
+group_id=1
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1,-2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -6,-1
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+##################################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=24
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors =  9, 15,  22, 18,  14, 39,  34, 41,  45, 86, 145,161 #416
+classes=3
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 23
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=24
+activation=linear
+
+[yolo]
+mask = 0,1,2
+anchors =  9, 15,  22, 18,  14, 39,  34, 41,  45, 86, 145,161 #416
+classes=3
+num=6
+jitter=.3
+scale_x_y = 1.05
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+resize=1.5
+nms_kind=greedynms
+beta_nms=0.6