added files

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+from fastai.vision.all import *
+import ultralytics
+from ultralytics import YOLO
+from PIL import Image, ImageDraw, ImageFont
+
+# import os
+# Load a pre-trained image classification model
+import pathlib
+plt = platform.system()
+if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
+if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
+
+root = os.path.dirname(__file__)
+
+detect = YOLO('./models/detect.pt')
+mood = load_learner("./models/mood.pkl")
+
+
+# Function to make predictions from an image
+def process(image):
+    boxes = detect.predict(image)
+    Image.open(image)
+    image_with_boxes = image.copy()
+    
+    draw = ImageDraw.Draw(image_with_boxes)
+
+    for i, box in enumerate(boxes[0].boxes.xyxy):
+        # print(box)
+        x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
+        
+        cropped_image = image.crop((x1, y1, x2, y2))
+        resized_image = cropped_image.resize((48, 48))
+        grayscale_image = resized_image.convert('L')
+
+        w = (y2+x2-y1-x1)//50
+        pil_image = PILImage.create(grayscale_image)
+        prediction = mood.predict(pil_image)
+        # print(prediction)
+        text = prediction[0]
+        text_position = (x1 + w, y1 + w)
+        draw.rectangle([x1, y1, x2, y2], outline="red", width=w)
+        font = ImageFont.truetype("opensans.ttf", 5*w)
+        draw.text(text_position, text, fill="blue",font=font, stroke_width=int(w*0.2))
+    
+    return image_with_boxes
+
+
+
+# Sample images for user to choose from
+sample_images = ["./sample_images/angry.jpg", "./sample_images/office.jpg","./sample_images/friends.jpg"]
+
+iface = gr.Interface(
+    fn=process,
+    inputs=gr.Image(label="Select an image", type="filepath"),
+    outputs='image',
+    live=False,
+    title="Car image classifier",
+    description="Upload a car image or select one of the examples below",
+    examples=sample_images
+)
+
+
+iface.launch()

detection_training/args.yaml

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+task: detect
+mode: train
+model: /content/yolov8n.pt
+data: data.yaml
+epochs: 10
+patience: 50
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: null
+workers: 8
+project: null
+name: null
+exist_ok: false
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
+save_json: false
+save_hybrid: false
+conf: null
+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
+source: null
+show: false
+save_txt: false
+save_conf: false
+save_crop: false
+show_labels: true
+show_conf: true
+vid_stride: 1
+stream_buffer: false
+line_width: null
+visualize: false
+augment: false
+agnostic_nms: false
+classes: null
+retina_masks: false
+boxes: true
+format: torchscript
+keras: false
+optimize: false
+int8: false
+dynamic: false
+simplify: false
+opset: null
+workspace: 4
+nms: false
+lr0: 0.01
+lrf: 0.01
+momentum: 0.937
+weight_decay: 0.0005
+warmup_epochs: 3.0
+warmup_momentum: 0.8
+warmup_bias_lr: 0.1
+box: 7.5
+cls: 0.5
+dfl: 1.5
+pose: 12.0
+kobj: 1.0
+label_smoothing: 0.0
+nbs: 64
+hsv_h: 0.015
+hsv_s: 0.7
+hsv_v: 0.4
+degrees: 0.0
+translate: 0.1
+scale: 0.5
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
+cfg: null
+tracker: botsort.yaml
+save_dir: runs/detect/train

detection_training/events.out.tfevents.1723263798.193db2adeb1d.880.0

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+size 1573123

detection_training/results.csv

@@ -0,0 +1,11 @@
+                  epoch,         train/box_loss,         train/cls_loss,         train/dfl_loss,   metrics/precision(B),      metrics/recall(B),       metrics/mAP50(B),    metrics/mAP50-95(B),           val/box_loss,           val/cls_loss,           val/dfl_loss,                 lr/pg0,                 lr/pg1,                 lr/pg2
+                      1,                 2.1183,                 1.8345,                 1.3011,                0.69467,                0.37721,                 0.4271,                0.21054,                 1.7273,                 1.0806,                 1.0973,             0.00066584,             0.00066584,             0.00066584
+                      2,                 1.7519,                  1.101,                 1.0979,                0.72355,                0.39829,                 0.4639,                0.23574,                  1.673,                0.99172,                 1.0819,              0.0012006,              0.0012006,              0.0012006
+                      3,                 1.6888,                 1.0075,                 1.0722,                 0.7492,                0.45081,                0.51379,                0.25759,                 1.5915,                0.90528,                 1.0444,              0.0016033,              0.0016033,              0.0016033
+                      4,                 1.6409,                0.95279,                 1.0577,                0.76166,                0.45202,                0.52456,                0.26817,                 1.5833,                0.87973,                 1.0401,               0.001406,               0.001406,               0.001406
+                      5,                 1.6188,                0.92058,                 1.0471,                0.76386,                0.46059,                0.53145,                0.27005,                 1.5789,                0.84541,                 1.0448,               0.001406,               0.001406,               0.001406
+                      6,                 1.5884,                0.88101,                 1.0381,                0.77804,                0.46672,                0.54119,                0.28155,                 1.5531,                0.81778,                 1.0327,               0.001208,               0.001208,               0.001208
+                      7,                 1.5645,                0.85025,                 1.0288,                0.78715,                0.48032,                0.54812,                0.28519,                 1.5269,                0.79034,                 1.0252,                0.00101,                0.00101,                0.00101
+                      8,                 1.5436,                0.82403,                 1.0207,                0.79692,                0.48759,                0.56661,                0.29809,                 1.4967,                0.77194,                  1.015,               0.000812,               0.000812,               0.000812
+                      9,                 1.5233,                0.80503,                 1.0121,                0.79457,                0.50006,                 0.5688,                0.29073,                 1.5153,                0.76072,                  1.016,               0.000614,               0.000614,               0.000614
+                     10,                 1.5007,                0.77753,                 1.0083,                0.79893,                0.50977,                0.58571,                0.31009,                 1.4566,                0.72831,                 1.0044,               0.000416,               0.000416,               0.000416

detection_training/weights/detect.pt

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detection_training/weights/last.pt

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models/detect.pt

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models/mood.pkl

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+size 88064574

mood_data.zip

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+size 63863343

requirements.txt.txt

@@ -0,0 +1,4 @@
+gradio
+fastai==2.7.14
+spacy==3.7.4
+ultralytics